{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/LaGina/Desktop/bjps_costlyprotest_replicationfile.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}27 Jun 2020, 18:53:01
{txt}
{com}. 
. ***************************************************************************
. * Title:    Revealing Issue Salience via Costly Protest:                                  *
. *                        How Legislative Behavior following Protest                                   *                                            
. *                               Advantages Low-Resource Groups                                            *
. * Author:       LaGina Gause (lgause@ucsd.edu)                                                        *
. * Date: 06/27/2020                                                        *
. * Software: Stata 14.2                                                    *
. ***************************************************************************
. 
. 
. 
. // set directory
. 
. /* 
> Required Dataset
> 
> bjps_costlyprotest_replication_data.csv
> */
. 
. set more off
{txt}
{com}. set matsize 800
{txt}
{com}. 
. *************************************
. ******** Summary Statistics *********
. *************************************
. 
. *ssc inst unique
. 
. tab protest majortopic 

           {txt}{c |} majortopic
   protest {c |}         2 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}    31,055 {txt}{c |}{res}    31,055 
{txt}         1 {c |}{res}     4,040 {txt}{c |}{res}     4,040 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}    35,095 {txt}{c |}{res}    35,095 

{txt}
{com}. 
. tab support if support!=.

    {txt}support {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,336       44.36       44.36
{txt}          1 {c |}{res}      1,676       55.64      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      3,012      100.00
{txt}
{com}. 
. unique eventid 
{txt}Number of unique values of eventid is  {res}612
{txt}Number of records is  {res}35095
{txt}
{com}. 
. unique eventid if support!=.
{txt}Number of unique values of eventid is  {res}503
{txt}Number of records is  {res}3012
{txt}
{com}. 
. display 503/612 
{res}.82189542
{txt}
{com}. 
. unique rccount cnrptyy if support!=.
{txt}Number of unique values of rccount cnrptyy is  {res}64
{txt}Number of records is  {res}3012
{txt}
{com}. 
. *****************************
. *****************************
. ****** Label Variables ******
. *****************************
. *****************************
. 
. label define ethnic 1 "Nonwhite Participants" 0 "White Participants"
{txt}
{com}. label values nonwhite ethnic
{txt}
{com}. 
. label define orgtype 1 "No Organized Interest Group" 0 "Organized Interest Group"
{txt}
{com}. label values noorgig orgtype
{txt}
{com}. 
. label define econ 1 "Low-Income Participants" 0 "Higher Income Participants" 
{txt}
{com}. label values poorh econ
{txt}
{com}. 
. 
. 
. label define ethnicc 2 "Nonwhite Participants" 1 "White Participants" 0 "No Protest"
{txt}
{com}. label values nonwhitec ethnicc
{txt}
{com}. 
. label define orgtypec 2 "No Organized Interest Group" 1 "Organized Interest Group" 0 "No Protest"
{txt}
{com}. label values noorgigc orgtypec
{txt}
{com}. 
. label define econc 2 "Low-Income Participants" 1 "Higher Income Participants" 0 "No Protest"
{txt}
{com}. label values poorhc econc
{txt}
{com}. 
. label define protestv 1 "Protest" 0 "No Protest"
{txt}
{com}. label values protest protestv
{txt}
{com}. 
. label variable protest "Protest"
{txt}
{com}. label variable educ "Education"
{txt}
{com}. label variable dem "Democratic Representative" 
{txt}
{com}. label variable lengthserve "Length of Service" 
{txt}
{com}. label variable votemargin "Margin of Victory" 
{txt}
{com}. label variable blkrep "Black Representative" 
{txt}
{com}. label variable hisprep "Latino Representative" 
{txt}
{com}. label variable womanrep "Female Representative"
{txt}
{com}. 
. 
. label define sizes 1 "Small" 2 "Group" 3 "Large" 4 "Hundreds" 5 "Thousands" 6 "Tens of Thousands"
{txt}
{com}. label values osize sizes
{txt}
{com}. 
. label define articles 1 "Multiple Articles" 0 "One Article"
{txt}
{com}. label values stories0 articles
{txt}
{com}. 
. label define contentious 1 "Disruptive" 0 "Not Disruptive"
{txt}
{com}. label values disruptive0 contentious
{txt}
{com}. 
. 
. ***********************************************
. ***********************************************
. ******* Table 1: Means and Differences ********
. ***********************************************
. ***********************************************
. 
. preserve
{txt}
{com}. keep if lib==1 
{txt}(979 observations deleted)

{com}. 
. mat T = J(14,4,.)
{txt}
{com}. 
. ttest supportall2, by(protest) 

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
No Prote {c |}{res}{col 12} 27,689{col 22} .4494926{col 34} .0029895{col 46} .4974514{col 58}  .443633{col 70} .4553521
 {txt}Protest {c |}{res}{col 12}  2,304{col 22} .5373264{col 34} .0103899{col 46}  .498713{col 58} .5169519{col 70} .5577008
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12} 29,993{col 22} .4562398{col 34} .0028761{col 46} .4980897{col 58} .4506026{col 70}  .461877
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0878338{col 34} .0107882{col 58}-.1089792{col 70}-.0666884
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No Prote{txt}) - mean({res}Protest{txt})                         t = {res} -8.1416
{txt}Ho: diff = 0                                     degrees of freedom = {res}   29991

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. mat T[1,1] = r(N_1)
{txt}
{com}. mat T[2,1] = r(N_2)
{txt}
{com}. mat T[1,2] = r(mu_1)
{txt}
{com}. mat T[2,2] = r(mu_2)
{txt}
{com}. mat T[2,3] = r(mu_1) - r(mu_2)
{txt}
{com}. mat T[2,4] = r(p)
{txt}
{com}. 
. ttest supportall2 if poorhc==0|poorhc==1, by(poorhc)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
No Prote {c |}{res}{col 12} 27,689{col 22} .4494926{col 34} .0029895{col 46} .4974514{col 58}  .443633{col 70} .4553521
{txt}Higher I {c |}{res}{col 12}  2,278{col 22} .5338016{col 34} .0104543{col 46} .4989657{col 58} .5133007{col 70} .5543025
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12} 29,967{col 22} .4559015{col 34} .0028771{col 46} .4980598{col 58} .4502622{col 70} .4615408
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} -.084309{col 34} .0108453{col 58}-.1055663{col 70}-.0630517
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No Prote{txt}) - mean({res}Higher I{txt})                        t = {res} -7.7738
{txt}Ho: diff = 0                                     degrees of freedom = {res}   29965

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. mat T[3,1] = r(N_1)
{txt}
{com}. mat T[4,1] = r(N_2)
{txt}
{com}. mat T[3,2] = r(mu_1)
{txt}
{com}. mat T[4,2] = r(mu_2)
{txt}
{com}. mat T[4,3] = r(mu_1) - r(mu_2)
{txt}
{com}. mat T[4,4] = r(p)
{txt}
{com}. 
. ttest supportall2 if poorhc==1|poorhc==2, by(poorhc)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Higher I {c |}{res}{col 12}  2,278{col 22} .5338016{col 34} .0104543{col 46} .4989657{col 58} .5133007{col 70} .5543025
{txt}Low-Inco {c |}{res}{col 12}     26{col 22} .8461538{col 34} .0721602{col 46} .3679465{col 58}  .697537{col 70} .9947706
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,304{col 22} .5373264{col 34} .0103899{col 46}  .498713{col 58} .5169519{col 70} .5577008
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.3123523{col 34}  .098168{col 58}-.5048592{col 70}-.1198454
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Higher I{txt}) - mean({res}Low-Inco{txt})                        t = {res} -3.1818
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2302

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0007         {txt}Pr(|T| > |t|) = {res}0.0015          {txt}Pr(T > t) = {res}0.9993
{txt}
{com}. mat T[5,1] = r(N_1)
{txt}
{com}. mat T[6,1] = r(N_2)
{txt}
{com}. mat T[5,2] = r(mu_1)
{txt}
{com}. mat T[6,2] = r(mu_2)
{txt}
{com}. mat T[6,3] = r(mu_1) - r(mu_2)
{txt}
{com}. mat T[6,4] = r(p)
{txt}
{com}. 
. ttest supportall2 if nonwhitec==0|nonwhitec==1, by(nonwhitec)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
No Prote {c |}{res}{col 12} 27,689{col 22} .4494926{col 34} .0029895{col 46} .4974514{col 58}  .443633{col 70} .4553521
{txt}White Pa {c |}{res}{col 12}  1,834{col 22} .5152672{col 34} .0116731{col 46} .4999032{col 58} .4923732{col 70} .5381611
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12} 29,523{col 22} .4535786{col 34} .0028975{col 46} .4978488{col 58} .4478994{col 70} .4592577
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0657746{col 34} .0119981{col 58}-.0892913{col 70}-.0422579
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No Prote{txt}) - mean({res}White Pa{txt})                        t = {res} -5.4821
{txt}Ho: diff = 0                                     degrees of freedom = {res}   29521

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. mat T[7,1] = r(N_1)
{txt}
{com}. mat T[8,1] = r(N_2)
{txt}
{com}. mat T[7,2] = r(mu_1)
{txt}
{com}. mat T[8,2] = r(mu_2)
{txt}
{com}. mat T[8,3] = r(mu_1) - r(mu_2)
{txt}
{com}. mat T[8,4] = r(p)
{txt}
{com}. 
. ttest supportall2 if nonwhitec==1|nonwhitec==2, by(nonwhitec)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
White Pa {c |}{res}{col 12}  1,834{col 22} .5152672{col 34} .0116731{col 46} .4999032{col 58} .4923732{col 70} .5381611
{txt}Nonwhite {c |}{res}{col 12}    470{col 22} .6234043{col 34} .0223736{col 46} .4850484{col 58} .5794393{col 70} .6673692
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,304{col 22} .5373264{col 34} .0103899{col 46}  .498713{col 58} .5169519{col 70} .5577008
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.1081371{col 34} .0256905{col 58} -.158516{col 70}-.0577581
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}White Pa{txt}) - mean({res}Nonwhite{txt})                        t = {res} -4.2092
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2302

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. mat T[9,1] = r(N_1)
{txt}
{com}. mat T[10,1] = r(N_2)
{txt}
{com}. mat T[9,2] = r(mu_1)
{txt}
{com}. mat T[10,2] = r(mu_2)
{txt}
{com}. mat T[10,3] = r(mu_1) - r(mu_2)
{txt}
{com}. mat T[10,4] = r(p)
{txt}
{com}. 
. ttest supportall2 if noorgigc==0|noorgigc==1, by(noorgigc)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
No Prote {c |}{res}{col 12} 27,689{col 22} .4494926{col 34} .0029895{col 46} .4974514{col 58}  .443633{col 70} .4553521
{txt}Organize {c |}{res}{col 12}    891{col 22} .5230079{col 34} .0167423{col 46} .4997509{col 58} .4901489{col 70} .5558668
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12} 28,580{col 22} .4517845{col 34} .0029439{col 46} .4976785{col 58} .4460144{col 70} .4575546
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0735153{col 34} .0169337{col 58}-.1067061{col 70}-.0403244
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No Prote{txt}) - mean({res}Organize{txt})                        t = {res} -4.3414
{txt}Ho: diff = 0                                     degrees of freedom = {res}   28578

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. mat T[11,1] = r(N_1)
{txt}
{com}. mat T[12,1] = r(N_2)
{txt}
{com}. mat T[11,2] = r(mu_1)
{txt}
{com}. mat T[12,2] = r(mu_2)
{txt}
{com}. mat T[12,3] = r(mu_1) - r(mu_2)
{txt}
{com}. mat T[12,4] = r(p)
{txt}
{com}. 
. ttest supportall2 if noorgigc==1|noorgigc==2, by(noorgigc)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Organize {c |}{res}{col 12}    891{col 22} .5230079{col 34} .0167423{col 46} .4997509{col 58} .4901489{col 70} .5558668
{txt}No Organ {c |}{res}{col 12}  1,397{col 22} .5433071{col 34} .0133319{col 46} .4982993{col 58} .5171544{col 70} .5694598
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,288{col 22} .5354021{col 34} .0104291{col 46} .4988541{col 58} .5149507{col 70} .5558535
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0202992{col 34} .0213882{col 58}-.0622415{col 70} .0216431
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Organize{txt}) - mean({res}No Organ{txt})                        t = {res} -0.9491
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2286

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.1713         {txt}Pr(|T| > |t|) = {res}0.3427          {txt}Pr(T > t) = {res}0.8287
{txt}
{com}. mat T[13,1] = r(N_1)
{txt}
{com}. mat T[14,1] = r(N_2)
{txt}
{com}. mat T[13,2] = r(mu_1)
{txt}
{com}. mat T[14,2] = r(mu_2)
{txt}
{com}. mat T[14,3] = r(mu_1) - r(mu_2)
{txt}
{com}. mat T[14,4] = r(p)
{txt}
{com}. 
. frmttable using example.tex,  statmat(T)  sdec(0,4)  replace ///
>         ctitle("", Observations, Mean Support, Difference, "(p-value)") ///
>         rtitle("No Protest"\"Protest"\ "No Protest"\ "Higher Income Participants" \"Higher Income Participants"\ "Low-Income Participants" ///
>         \"No Protest" \ "White Participants" \ "White Participants" \ "Non-White Participants" \ "No Protest" \ "Organized Interest Group" ///
>         \"Organized Interest Group" \ "No Organized Interest Group")
{res}{txt:(note: file example.tex not found)}
{txt}{center:{hline 82}}
{center:{txt}{lalign 29:}{txt}{center 14:Observations}{txt}{center 14:Mean Support}{txt}{center 12:Difference}{txt}{center 11:(p-value)}}
{txt}{center:{hline 82}}
{center:{txt}{lalign 29:No Protest}{res}{center 14:27,689}{res}{center 14:0.4495}{res}{center 12:}{res}{center 11:}}
{center:{txt}{lalign 29:Protest}{res}{center 14:2,304}{res}{center 14:0.5373}{res}{center 12:-0.0878}{res}{center 11:0.0000}}
{center:{txt}{lalign 29:No Protest}{res}{center 14:27,689}{res}{center 14:0.4495}{res}{center 12:}{res}{center 11:}}
{center:{txt}{lalign 29:Higher Income Participants}{res}{center 14:2,278}{res}{center 14:0.5338}{res}{center 12:-0.0843}{res}{center 11:0.0000}}
{center:{txt}{lalign 29:Higher Income Participants}{res}{center 14:2,278}{res}{center 14:0.5338}{res}{center 12:}{res}{center 11:}}
{center:{txt}{lalign 29:Low-Income Participants}{res}{center 14:26}{res}{center 14:0.8462}{res}{center 12:-0.3124}{res}{center 11:0.0015}}
{center:{txt}{lalign 29:No Protest}{res}{center 14:27,689}{res}{center 14:0.4495}{res}{center 12:}{res}{center 11:}}
{center:{txt}{lalign 29:White Participants}{res}{center 14:1,834}{res}{center 14:0.5153}{res}{center 12:-0.0658}{res}{center 11:0.0000}}
{center:{txt}{lalign 29:White Participants}{res}{center 14:1,834}{res}{center 14:0.5153}{res}{center 12:}{res}{center 11:}}
{center:{txt}{lalign 29:Non-White Participants}{res}{center 14:470}{res}{center 14:0.6234}{res}{center 12:-0.1081}{res}{center 11:0.0000}}
{center:{txt}{lalign 29:No Protest}{res}{center 14:27,689}{res}{center 14:0.4495}{res}{center 12:}{res}{center 11:}}
{center:{txt}{lalign 29:Organized Interest Group}{res}{center 14:891}{res}{center 14:0.5230}{res}{center 12:-0.0735}{res}{center 11:0.0000}}
{center:{txt}{lalign 29:Organized Interest Group}{res}{center 14:891}{res}{center 14:0.5230}{res}{center 12:}{res}{center 11:}}
{center:{txt}{lalign 29:No Organized Interest Group}{res}{center 14:1,397}{res}{center 14:0.5433}{res}{center 12:-0.0203}{res}{center 11:0.3427}}
{txt}{center:{hline 82}}


{com}. restore
{txt}
{com}. 
. 
. **************************************
. **************************************
. ***** Model Specification Checks *****
. **************************************
. **************************************
. encode statecd, gen(statecd1)
{txt}
{com}. 
. **** Fixed Effects Model ****
. **** Fixed Effects Model ****
. **** Fixed Effects Model ****
. eststo pfe2, title("Model I"): logit supportall2 i.poorhc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy i.statecd1 if lib==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-20597.969}  
Iteration 1:{space 3}log likelihood = {res:-19886.915}  
Iteration 2:{space 3}log likelihood = {res:-19882.632}  
Iteration 3:{space 3}log likelihood = {res:-19882.614}  
Iteration 4:{space 3}log likelihood = {res:-19882.614}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,882
{txt}{col 49}LR chi2({res}463{txt}){col 67}= {res}   1430.71
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-19882.614{txt}{col 49}Pseudo R2{col 67}= {res}    0.0347

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                supportall2{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}poorhc {c |}
Higher Income Participants  {c |}{col 29}{res}{space 2} .4299685{col 41}{space 2} .0649862{col 52}{space 1}    6.62{col 61}{space 3}0.000{col 69}{space 4} .3025979{col 82}{space 3}  .557339
{txt}{space 3}Low-Income Participants  {c |}{col 29}{res}{space 2} 2.112822{col 41}{space 2} .5510627{col 52}{space 1}    3.83{col 61}{space 3}0.000{col 69}{space 4} 1.032759{col 82}{space 3} 3.192885
{txt}{space 27} {c |}
{space 23}educ {c |}{col 29}{res}{space 2}-.0927094{col 41}{space 2} .2119276{col 52}{space 1}   -0.44{col 61}{space 3}0.662{col 69}{space 4}-.5080798{col 82}{space 3}  .322661
{txt}{space 24}dem {c |}{col 29}{res}{space 2} .4212327{col 41}{space 2} .0541976{col 52}{space 1}    7.77{col 61}{space 3}0.000{col 69}{space 4} .3150074{col 82}{space 3} .5274581
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{txt}{space 22}6 48  {c |}{col 29}{res}{space 2} .8884821{col 41}{space 2} .3990911{col 52}{space 1}    2.23{col 61}{space 3}0.026{col 69}{space 4} .1062779{col 82}{space 3} 1.670686
{txt}{space 22}6 49  {c |}{col 29}{res}{space 2}  .604419{col 41}{space 2} .4016055{col 52}{space 1}    1.51{col 61}{space 3}0.132{col 69}{space 4}-.1827133{col 82}{space 3} 1.391551
{txt}{space 23}6 5  {c |}{col 29}{res}{space 2} .2808104{col 41}{space 2} .3715133{col 52}{space 1}    0.76{col 61}{space 3}0.450{col 69}{space 4}-.4473422{col 82}{space 3} 1.008963
{txt}{space 22}6 50  {c |}{col 29}{res}{space 2} .3825137{col 41}{space 2} .3982032{col 52}{space 1}    0.96{col 61}{space 3}0.337{col 69}{space 4}-.3979503{col 82}{space 3} 1.162978
{txt}{space 22}6 51  {c |}{col 29}{res}{space 2} .9258191{col 41}{space 2} .3957274{col 52}{space 1}    2.34{col 61}{space 3}0.019{col 69}{space 4} .1502078{col 82}{space 3} 1.701431
{txt}{space 22}6 52  {c |}{col 29}{res}{space 2} .2549462{col 41}{space 2} .3951325{col 52}{space 1}    0.65{col 61}{space 3}0.519{col 69}{space 4}-.5194992{col 82}{space 3} 1.029392
{txt}{space 23}6 6  {c |}{col 29}{res}{space 2} .0357152{col 41}{space 2} .3853718{col 52}{space 1}    0.09{col 61}{space 3}0.926{col 69}{space 4}-.7195997{col 82}{space 3} .7910301
{txt}{space 23}6 7  {c |}{col 29}{res}{space 2} .4478795{col 41}{space 2} .3719288{col 52}{space 1}    1.20{col 61}{space 3}0.229{col 69}{space 4}-.2810875{col 82}{space 3} 1.176847
{txt}{space 23}6 8  {c |}{col 29}{res}{space 2} .2226837{col 41}{space 2} .3877024{col 52}{space 1}    0.57{col 61}{space 3}0.566{col 69}{space 4} -.537199{col 82}{space 3} .9825663
{txt}{space 23}6 9  {c |}{col 29}{res}{space 2} .5956307{col 41}{space 2} .3824957{col 52}{space 1}    1.56{col 61}{space 3}0.119{col 69}{space 4}-.1540471{col 82}{space 3} 1.345309
{txt}{space 23}8 1  {c |}{col 29}{res}{space 2} .2263652{col 41}{space 2}  .377712{col 52}{space 1}    0.60{col 61}{space 3}0.549{col 69}{space 4}-.5139367{col 82}{space 3}  .966667
{txt}{space 23}8 2  {c |}{col 29}{res}{space 2} .6279995{col 41}{space 2} .3704725{col 52}{space 1}    1.70{col 61}{space 3}0.090{col 69}{space 4}-.0981132{col 82}{space 3} 1.354112
{txt}{space 23}8 3  {c |}{col 29}{res}{space 2} .2726995{col 41}{space 2} .3687513{col 52}{space 1}    0.74{col 61}{space 3}0.460{col 69}{space 4}-.4500397{col 82}{space 3} .9954388
{txt}{space 23}8 4  {c |}{col 29}{res}{space 2}-.0444266{col 41}{space 2} .3730252{col 52}{space 1}   -0.12{col 61}{space 3}0.905{col 69}{space 4}-.7755425{col 82}{space 3} .6866893
{txt}{space 23}8 5  {c |}{col 29}{res}{space 2}-.2378225{col 41}{space 2} .3804681{col 52}{space 1}   -0.63{col 61}{space 3}0.532{col 69}{space 4}-.9835263{col 82}{space 3} .5078813
{txt}{space 23}8 6  {c |}{col 29}{res}{space 2} -.206324{col 41}{space 2} .3874983{col 52}{space 1}   -0.53{col 61}{space 3}0.594{col 69}{space 4}-.9658067{col 82}{space 3} .5531587
{txt}{space 23}9 1  {c |}{col 29}{res}{space 2} .2587895{col 41}{space 2}  .376074{col 52}{space 1}    0.69{col 61}{space 3}0.491{col 69}{space 4}-.4783019{col 82}{space 3}  .995881
{txt}{space 23}9 2  {c |}{col 29}{res}{space 2} .9349396{col 41}{space 2} .4032115{col 52}{space 1}    2.32{col 61}{space 3}0.020{col 69}{space 4} .1446595{col 82}{space 3}  1.72522
{txt}{space 23}9 3  {c |}{col 29}{res}{space 2} .2298169{col 41}{space 2}  .374939{col 52}{space 1}    0.61{col 61}{space 3}0.540{col 69}{space 4}-.5050501{col 82}{space 3} .9646839
{txt}{space 23}9 4  {c |}{col 29}{res}{space 2} .4939275{col 41}{space 2} .3693387{col 52}{space 1}    1.34{col 61}{space 3}0.181{col 69}{space 4}-.2299631{col 82}{space 3} 1.217818
{txt}{space 23}9 5  {c |}{col 29}{res}{space 2} 1.129885{col 41}{space 2} .3870357{col 52}{space 1}    2.92{col 61}{space 3}0.004{col 69}{space 4} .3713093{col 82}{space 3} 1.888461
{txt}{space 23}9 6  {c |}{col 29}{res}{space 2} .3576022{col 41}{space 2} .3740718{col 52}{space 1}    0.96{col 61}{space 3}0.339{col 69}{space 4}-.3755651{col 82}{space 3}  1.09077
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2}-1.563209{col 41}{space 2} .2933807{col 52}{space 1}   -5.33{col 61}{space 3}0.000{col 69}{space 4}-2.138225{col 82}{space 3}-.9881938
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. **** Random Effects models ****
. **** Random Effects models ****
. **** Random Effects models ****
. eststo pre2, title("Model I"):  melogit supportall2 i.poorhc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy  if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    29,882
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       453

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        13
{col 63}{txt}avg{col 67}={res}{col 69}      66.0
{col 63}{txt}max{col 67}={res}{col 69}       137

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   426.90
{txt}Log likelihood = {res}-20256.582{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 supportall2{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 28} {c |}
{space 22}poorhc {c |}
{space 1}Higher Income Participants  {c |}{col 30}{res}{space 2} .2820792{col 42}{space 2} .0544773{col 53}{space 1}    5.18{col 62}{space 3}0.000{col 70}{space 4} .1753056{col 83}{space 3} .3888528
{txt}{space 4}Low-Income Participants  {c |}{col 30}{res}{space 2} 1.883899{col 42}{space 2} .5482357{col 53}{space 1}    3.44{col 62}{space 3}0.001{col 70}{space 4} .8093769{col 83}{space 3} 2.958421
{txt}{space 28} {c |}
{space 24}educ {c |}{col 30}{res}{space 2} .1695239{col 42}{space 2} .1037669{col 53}{space 1}    1.63{col 62}{space 3}0.102{col 70}{space 4}-.0338554{col 83}{space 3} .3729032
{txt}{space 25}dem {c |}{col 30}{res}{space 2} .5300756{col 42}{space 2} .0313947{col 53}{space 1}   16.88{col 62}{space 3}0.000{col 70}{space 4} .4685431{col 83}{space 3} .5916082
{txt}{space 17}lengthserve {c |}{col 30}{res}{space 2}-.0939462{col 42}{space 2} .0896436{col 53}{space 1}   -1.05{col 62}{space 3}0.295{col 70}{space 4}-.2696444{col 83}{space 3} .0817521
{txt}{space 18}votemargin {c |}{col 30}{res}{space 2} .3502199{col 42}{space 2}  .110737{col 53}{space 1}    3.16{col 62}{space 3}0.002{col 70}{space 4} .1331794{col 83}{space 3} .5672604
{txt}{space 22}blkrep {c |}{col 30}{res}{space 2}-.0953413{col 42}{space 2} .0577456{col 53}{space 1}   -1.65{col 62}{space 3}0.099{col 70}{space 4}-.2085206{col 83}{space 3} .0178381
{txt}{space 21}hisprep {c |}{col 30}{res}{space 2} .1282645{col 42}{space 2} .0806538{col 53}{space 1}    1.59{col 62}{space 3}0.112{col 70}{space 4} -.029814{col 83}{space 3}  .286343
{txt}{space 20}womanrep {c |}{col 30}{res}{space 2} .1585816{col 42}{space 2} .0499011{col 53}{space 1}    3.18{col 62}{space 3}0.001{col 70}{space 4} .0607773{col 83}{space 3} .2563858
{txt}{space 28} {c |}
{space 21}cnrptyy {c |}
{space 24}103  {c |}{col 30}{res}{space 2} .1240051{col 42}{space 2} .0312904{col 53}{space 1}    3.96{col 62}{space 3}0.000{col 70}{space 4} .0626771{col 83}{space 3} .1853332
{txt}{space 24}104  {c |}{col 30}{res}{space 2} .0795082{col 42}{space 2} .0335722{col 53}{space 1}    2.37{col 62}{space 3}0.018{col 70}{space 4}  .013708{col 83}{space 3} .1453084
{txt}{space 28} {c |}
{space 23}_cons {c |}{col 30}{res}{space 2}-.8700015{col 42}{space 2} .0886514{col 53}{space 1}   -9.81{col 62}{space 3}0.000{col 70}{space 4}-1.043755{col 83}{space 3}-.6962479
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                     {col 30}{c |}
                   var(_cons){c |}{col 30}{res}{space 2} .0452625{col 42}{space 2} .0077736{col 70}{space 4} .0323257{col 83}{space 3} .0633765
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 70.46{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. 
. 
. **** Pooled Regression Models ****
. **** Pooled Regression Models ****
. **** Pooled Regression Models ****
. 
. eststo pp2, title("Model I"):  logit supportall2 i.poorhc educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1,  nolog
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,882
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}    612.31
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-20291.814{txt}{col 49}Pseudo R2{col 67}= {res}    0.0149

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                supportall2{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}poorhc {c |}
Higher Income Participants  {c |}{col 29}{res}{space 2}  .191745{col 41}{space 2} .0458927{col 52}{space 1}    4.18{col 61}{space 3}0.000{col 69}{space 4}  .101797{col 82}{space 3} .2816931
{txt}{space 3}Low-Income Participants  {c |}{col 29}{res}{space 2} 1.732811{col 41}{space 2} .5454617{col 52}{space 1}    3.18{col 61}{space 3}0.001{col 69}{space 4} .6637254{col 82}{space 3} 2.801896
{txt}{space 27} {c |}
{space 23}educ {c |}{col 29}{res}{space 2} .2062731{col 41}{space 2}  .082995{col 52}{space 1}    2.49{col 61}{space 3}0.013{col 69}{space 4} .0436059{col 82}{space 3} .3689403
{txt}{space 24}dem {c |}{col 29}{res}{space 2} .5323615{col 41}{space 2} .0257718{col 52}{space 1}   20.66{col 61}{space 3}0.000{col 69}{space 4} .4818497{col 82}{space 3} .5828733
{txt}{space 16}lengthserve {c |}{col 29}{res}{space 2}-.0448525{col 41}{space 2} .0751663{col 52}{space 1}   -0.60{col 61}{space 3}0.551{col 69}{space 4}-.1921757{col 82}{space 3} .1024708
{txt}{space 17}votemargin {c |}{col 29}{res}{space 2} .0839876{col 41}{space 2}  .092493{col 52}{space 1}    0.91{col 61}{space 3}0.364{col 69}{space 4}-.0972953{col 82}{space 3} .2652705
{txt}{space 21}blkrep {c |}{col 29}{res}{space 2}-.0353742{col 41}{space 2} .0462326{col 52}{space 1}   -0.77{col 61}{space 3}0.444{col 69}{space 4}-.1259884{col 82}{space 3} .0552401
{txt}{space 20}hisprep {c |}{col 29}{res}{space 2} .1474767{col 41}{space 2} .0636866{col 52}{space 1}    2.32{col 61}{space 3}0.021{col 69}{space 4} .0226532{col 82}{space 3} .2723003
{txt}{space 19}womanrep {c |}{col 29}{res}{space 2}   .14767{col 41}{space 2} .0405357{col 52}{space 1}    3.64{col 61}{space 3}0.000{col 69}{space 4} .0682215{col 82}{space 3} .2271184
{txt}{space 27} {c |}
{space 20}cnrptyy {c |}
{space 23}103  {c |}{col 29}{res}{space 2} .1095334{col 41}{space 2} .0303418{col 52}{space 1}    3.61{col 61}{space 3}0.000{col 69}{space 4} .0500646{col 82}{space 3} .1690022
{txt}{space 23}104  {c |}{col 29}{res}{space 2} .0742912{col 41}{space 2} .0327352{col 52}{space 1}    2.27{col 61}{space 3}0.023{col 69}{space 4} .0101314{col 82}{space 3} .1384511
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2}-.7102104{col 41}{space 2} .0749614{col 52}{space 1}   -9.47{col 61}{space 3}0.000{col 69}{space 4}-.8571321{col 82}{space 3}-.5632888
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. hausman pfe2 pre2, alleqs

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}    pfe2    {col 30}    pre2    {col 46}Difference{col 66}S.E.
{hline 13}{c +}{hline 64}
{space 6}poorhc {c |}
{space 10}1  {c |}{res}{col 18} .4299685{col 31} .2820792{col 47} .1478892{col 63}  .035432
{txt}{space 10}2  {c |}{res}{col 18} 2.112822{col 31} 1.883899{col 47} .2289231{col 63} .0557464
{txt}{space 8}educ {c |}{res}{col 18}-.0927094{col 31} .1695239{col 47}-.2622333{col 63} .1847857
{txt}{space 9}dem {c |}{res}{col 18} .4212327{col 31} .5300756{col 47}-.1088429{col 63} .0441786
{txt}{space 1}lengthserve {c |}{res}{col 18}-.1718387{col 31}-.0939462{col 47}-.0778926{col 63} .1063394
{txt}{space 2}votemargin {c |}{res}{col 18} 1.143948{col 31} .3502199{col 47} .7937281{col 63} .0825968
{txt}{space 6}blkrep {c |}{res}{col 18}-.3876364{col 31}-.0953413{col 47}-.2922951{col 63} .0934455
{txt}{space 5}hisprep {c |}{res}{col 18}-.1349252{col 31} .1282645{col 47}-.2631897{col 63} .1565503
{txt}{space 4}womanrep {c |}{res}{col 18} .1696631{col 31} .1585816{col 47} .0110816{col 63} .0706451
{txt}{space 5}cnrptyy {c |}
{space 8}103  {c |}{res}{col 18} .1880845{col 31} .1240051{col 47} .0640793{col 63} .0179779
{txt}{space 8}104  {c |}{res}{col 18} .1078366{col 31} .0795082{col 47} .0283284{col 63} .0187731
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = consistent under Ho and Ha; obtained from logit}
{ralign 78:B = inconsistent under Ha, efficient under Ho; obtained from melogit}

    Test:  Ho:  difference in coefficients not systematic

{ralign 25:chi2({res:11})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 27}=  {res}    125.86
{txt}{col 17}Prob>chi2 =  {res}    0.0000
{txt}
{com}. 
. 
. ******* WU (1973) Variable Addition Test *********
. ******* WU (1973) Variable Addition Test *********
. ******* WU (1973) Variable Addition Test *********
. 
. **** Gather means for fixed effects variables (vary across time)
. egen educm = mean(educ), by(statecd1)
{txt}(70 missing values generated)

{com}. egen lenm = mean(lengthserve), by(statecd1)
{txt}(70 missing values generated)

{com}. egen margm = mean(votemargin), by(statecd1)
{txt}(70 missing values generated)

{com}. 
. melogit supportall2 poorhc nonwhitec noorgigc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy educm lenm margm if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    29,866
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       453

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        13
{col 63}{txt}avg{col 67}={res}{col 69}      65.9
{col 63}{txt}max{col 67}={res}{col 69}       136

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   524.28
{txt}Log likelihood = {res}-20211.593{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} supportall2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}poorhc {c |}{col 14}{res}{space 2} .0306832{col 26}{space 2} .1794239{col 37}{space 1}    0.17{col 46}{space 3}0.864{col 54}{space 4}-.3209812{col 67}{space 3} .3823476
{txt}{space 3}nonwhitec {c |}{col 14}{res}{space 2} .2865947{col 26}{space 2} .1063429{col 37}{space 1}    2.70{col 46}{space 3}0.007{col 54}{space 4} .0781664{col 67}{space 3} .4950229
{txt}{space 4}noorgigc {c |}{col 14}{res}{space 2}-.0283494{col 26}{space 2} .0824749{col 37}{space 1}   -0.34{col 46}{space 3}0.731{col 54}{space 4}-.1899972{col 67}{space 3} .1332984
{txt}{space 8}educ {c |}{col 14}{res}{space 2} .1071183{col 26}{space 2} .1981494{col 37}{space 1}    0.54{col 46}{space 3}0.589{col 54}{space 4}-.2812475{col 67}{space 3}  .495484
{txt}{space 9}dem {c |}{col 14}{res}{space 2} .5158233{col 26}{space 2} .0304594{col 37}{space 1}   16.93{col 46}{space 3}0.000{col 54}{space 4}  .456124{col 67}{space 3} .5755225
{txt}{space 1}lengthserve {c |}{col 14}{res}{space 2}-.2359554{col 26}{space 2} .1296981{col 37}{space 1}   -1.82{col 46}{space 3}0.069{col 54}{space 4} -.490159{col 67}{space 3} .0182482
{txt}{space 2}votemargin {c |}{col 14}{res}{space 2}  1.00722{col 26}{space 2} .1345164{col 37}{space 1}    7.49{col 46}{space 3}0.000{col 54}{space 4} .7435724{col 67}{space 3} 1.270867
{txt}{space 6}blkrep {c |}{col 14}{res}{space 2}-.0091505{col 26}{space 2} .0562435{col 37}{space 1}   -0.16{col 46}{space 3}0.871{col 54}{space 4}-.1193858{col 67}{space 3} .1010847
{txt}{space 5}hisprep {c |}{col 14}{res}{space 2} .1803494{col 26}{space 2} .0776527{col 37}{space 1}    2.32{col 46}{space 3}0.020{col 54}{space 4} .0281529{col 67}{space 3} .3325459
{txt}{space 4}womanrep {c |}{col 14}{res}{space 2} .1322668{col 26}{space 2} .0481699{col 37}{space 1}    2.75{col 46}{space 3}0.006{col 54}{space 4} .0378555{col 67}{space 3} .2266781
{txt}{space 12} {c |}
{space 5}cnrptyy {c |}
{space 8}103  {c |}{col 14}{res}{space 2} .1480931{col 26}{space 2} .0337586{col 37}{space 1}    4.39{col 46}{space 3}0.000{col 54}{space 4} .0819275{col 67}{space 3} .2142587
{txt}{space 8}104  {c |}{col 14}{res}{space 2} .0821501{col 26}{space 2} .0360735{col 37}{space 1}    2.28{col 46}{space 3}0.023{col 54}{space 4} .0114472{col 67}{space 3} .1528529
{txt}{space 12} {c |}
{space 7}educm {c |}{col 14}{res}{space 2} .0992334{col 26}{space 2} .2231622{col 37}{space 1}    0.44{col 46}{space 3}0.657{col 54}{space 4}-.3381564{col 67}{space 3} .5366232
{txt}{space 8}lenm {c |}{col 14}{res}{space 2}  .332019{col 26}{space 2} .1667313{col 37}{space 1}    1.99{col 46}{space 3}0.046{col 54}{space 4} .0052316{col 67}{space 3} .6588064
{txt}{space 7}margm {c |}{col 14}{res}{space 2}-1.691294{col 26}{space 2} .2043635{col 37}{space 1}   -8.28{col 46}{space 3}0.000{col 54}{space 4} -2.09184{col 67}{space 3}-1.290749
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2478341{col 26}{space 2} .1177189{col 37}{space 1}   -2.11{col 46}{space 3}0.035{col 54}{space 4}-.4785588{col 67}{space 3}-.0171093
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .0339788{col 26}{space 2} .0066759{col 54}{space 4} .0231191{col 67}{space 3} .0499398
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 47.22{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. 
. test  educm lenm margm 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[supportall2]educm = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [supportall2]lenm = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [supportall2]margm = 0{p_end}

{txt}{col 12}chi2(  3) ={res}   70.96
{txt}{col 10}Prob > chi2 =  {res}  0.0000
{txt}
{com}. 
. reg supportall2 i.poorhc educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy  i.statecd1 if lib==1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    29,882
{txt}{hline 13}{c +}{hline 34}   F(463, 29418)   = {res}     3.09
{txt}       Model {c |} {res} 343.774603       463  .742493743   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  7069.4714    29,418  .240311082   {txt}R-squared       ={res}    0.0464
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0314
{txt}       Total {c |} {res}   7413.246    29,881  .248092299   {txt}Root MSE        =   {res} .49022

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                supportall2{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      t{col 61}   P>|t|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}poorhc {c |}
Higher Income Participants  {c |}{col 29}{res}{space 2} .1035604{col 41}{space 2} .0155489{col 52}{space 1}    6.66{col 61}{space 3}0.000{col 69}{space 4} .0730839{col 82}{space 3} .1340369
{txt}{space 3}Low-Income Participants  {c |}{col 29}{res}{space 2} .4373008{col 41}{space 2} .0976712{col 52}{space 1}    4.48{col 61}{space 3}0.000{col 69}{space 4} .2458608{col 82}{space 3} .6287408
{txt}{space 27} {c |}
{space 23}educ {c |}{col 29}{res}{space 2}-.0212445{col 41}{space 2} .0506075{col 52}{space 1}   -0.42{col 61}{space 3}0.675{col 69}{space 4}-.1204374{col 82}{space 3} .0779484
{txt}{space 24}dem {c |}{col 29}{res}{space 2} .1019764{col 41}{space 2} .0129972{col 52}{space 1}    7.85{col 61}{space 3}0.000{col 69}{space 4} .0765014{col 82}{space 3} .1274515
{txt}{space 16}lengthserve {c |}{col 29}{res}{space 2}-.0420052{col 41}{space 2} .0333227{col 52}{space 1}   -1.26{col 61}{space 3}0.207{col 69}{space 4}-.1073192{col 82}{space 3} .0233089
{txt}{space 17}votemargin {c |}{col 29}{res}{space 2} .2690471{col 41}{space 2} .0326543{col 52}{space 1}    8.24{col 61}{space 3}0.000{col 69}{space 4} .2050432{col 82}{space 3} .3330511
{txt}{space 21}blkrep {c |}{col 29}{res}{space 2}-.0919575{col 41}{space 2}  .026344{col 52}{space 1}   -3.49{col 61}{space 3}0.000{col 69}{space 4} -.143593{col 82}{space 3}-.0403221
{txt}{space 20}hisprep {c |}{col 29}{res}{space 2}-.0299968{col 41}{space 2} .0423367{col 52}{space 1}   -0.71{col 61}{space 3}0.479{col 69}{space 4}-.1129786{col 82}{space 3} .0529851
{txt}{space 19}womanrep {c |}{col 29}{res}{space 2} .0405957{col 41}{space 2} .0206681{col 52}{space 1}    1.96{col 61}{space 3}0.050{col 69}{space 4} .0000852{col 82}{space 3} .0811062
{txt}{space 27} {c |}
{space 20}cnrptyy {c |}
{space 23}103  {c |}{col 29}{res}{space 2} .0442487{col 41}{space 2} .0085566{col 52}{space 1}    5.17{col 61}{space 3}0.000{col 69}{space 4} .0274774{col 82}{space 3} .0610201
{txt}{space 23}104  {c |}{col 29}{res}{space 2} .0242971{col 41}{space 2} .0091265{col 52}{space 1}    2.66{col 61}{space 3}0.008{col 69}{space 4} .0064087{col 82}{space 3} .0421854
{txt}{space 27} {c |}
{space 19}statecd1 {c |}
{space 23}1 2  {c |}{col 29}{res}{space 2}-.0518673{col 41}{space 2} .0862402{col 52}{space 1}   -0.60{col 61}{space 3}0.548{col 69}{space 4}-.2209019{col 82}{space 3} .1171673
{txt}{space 23}1 3  {c |}{col 29}{res}{space 2} .0266178{col 41}{space 2} .0872702{col 52}{space 1}    0.31{col 61}{space 3}0.760{col 69}{space 4}-.1444357{col 82}{space 3} .1976714
{txt}{space 23}1 4  {c |}{col 29}{res}{space 2} .0296506{col 41}{space 2} .0884589{col 52}{space 1}    0.34{col 61}{space 3}0.737{col 69}{space 4}-.1437328{col 82}{space 3} .2030341
{txt}{space 23}1 5  {c |}{col 29}{res}{space 2}  .033393{col 41}{space 2} .0878034{col 52}{space 1}    0.38{col 61}{space 3}0.704{col 69}{space 4}-.1387055{col 82}{space 3} .2054916
{txt}{space 23}1 6  {c |}{col 29}{res}{space 2}-.0555791{col 41}{space 2} .0865218{col 52}{space 1}   -0.64{col 61}{space 3}0.521{col 69}{space 4}-.2251656{col 82}{space 3} .1140075
{txt}{space 23}1 7  {c |}{col 29}{res}{space 2} .1189125{col 41}{space 2} .0889733{col 52}{space 1}    1.34{col 61}{space 3}0.181{col 69}{space 4}-.0554791{col 82}{space 3}  .293304
{txt}{space 22}10 1  {c |}{col 29}{res}{space 2} .1167507{col 41}{space 2} .0859495{col 52}{space 1}    1.36{col 61}{space 3}0.174{col 69}{space 4}-.0517141{col 82}{space 3} .2852155
{txt}{space 22}12 1  {c |}{col 29}{res}{space 2}-.0061684{col 41}{space 2} .0877255{col 52}{space 1}   -0.07{col 61}{space 3}0.944{col 69}{space 4}-.1781142{col 82}{space 3} .1657775
{txt}{space 21}12 10  {c |}{col 29}{res}{space 2} .0501347{col 41}{space 2} .0874638{col 52}{space 1}    0.57{col 61}{space 3}0.567{col 69}{space 4}-.1212982{col 82}{space 3} .2215677
{txt}{space 21}12 11  {c |}{col 29}{res}{space 2} .0924788{col 41}{space 2}  .089509{col 52}{space 1}    1.03{col 61}{space 3}0.302{col 69}{space 4}-.0829629{col 82}{space 3} .2679204
{txt}{space 21}12 12  {c |}{col 29}{res}{space 2} .0826513{col 41}{space 2} .0863425{col 52}{space 1}    0.96{col 61}{space 3}0.338{col 69}{space 4}-.0865838{col 82}{space 3} .2518865
{txt}{space 21}12 13  {c |}{col 29}{res}{space 2}-.0909634{col 41}{space 2} .0860661{col 52}{space 1}   -1.06{col 61}{space 3}0.291{col 69}{space 4}-.2596569{col 82}{space 3} .0777301
{txt}{space 21}12 14  {c |}{col 29}{res}{space 2}-.0429249{col 41}{space 2} .0863745{col 52}{space 1}   -0.50{col 61}{space 3}0.619{col 69}{space 4}-.2122229{col 82}{space 3} .1263731
{txt}{space 21}12 15  {c |}{col 29}{res}{space 2} .1884758{col 41}{space 2} .0868388{col 52}{space 1}    2.17{col 61}{space 3}0.030{col 69}{space 4} .0182678{col 82}{space 3} .3586837
{txt}{space 21}12 16  {c |}{col 29}{res}{space 2} .1204556{col 41}{space 2}  .088576{col 52}{space 1}    1.36{col 61}{space 3}0.174{col 69}{space 4}-.0531573{col 82}{space 3} .2940685
{txt}{space 21}12 17  {c |}{col 29}{res}{space 2} .1426306{col 41}{space 2}   .09129{col 52}{space 1}    1.56{col 61}{space 3}0.118{col 69}{space 4}-.0363019{col 82}{space 3} .3215632
{txt}{space 21}12 18  {c |}{col 29}{res}{space 2} .1797885{col 41}{space 2} .0989833{col 52}{space 1}    1.82{col 61}{space 3}0.069{col 69}{space 4}-.0142233{col 82}{space 3} .3738003
{txt}{space 21}12 19  {c |}{col 29}{res}{space 2} .0651232{col 41}{space 2} .0878324{col 52}{space 1}    0.74{col 61}{space 3}0.458{col 69}{space 4}-.1070322{col 82}{space 3} .2372786
{txt}{space 22}12 2  {c |}{col 29}{res}{space 2} .0581738{col 41}{space 2} .0874365{col 52}{space 1}    0.67{col 61}{space 3}0.506{col 69}{space 4}-.1132056{col 82}{space 3} .2295532
{txt}{space 21}12 20  {c |}{col 29}{res}{space 2}  .056153{col 41}{space 2} .0942569{col 52}{space 1}    0.60{col 61}{space 3}0.551{col 69}{space 4}-.1285948{col 82}{space 3} .2409007
{txt}{space 21}12 21  {c |}{col 29}{res}{space 2} .1771528{col 41}{space 2} .1007447{col 52}{space 1}    1.76{col 61}{space 3}0.079{col 69}{space 4}-.0203114{col 82}{space 3} .3746169
{txt}{space 21}12 22  {c |}{col 29}{res}{space 2} .0331659{col 41}{space 2} .0914846{col 52}{space 1}    0.36{col 61}{space 3}0.717{col 69}{space 4}-.1461481{col 82}{space 3} .2124799
{txt}{space 21}12 23  {c |}{col 29}{res}{space 2}  .039428{col 41}{space 2} .0977059{col 52}{space 1}    0.40{col 61}{space 3}0.687{col 69}{space 4}-.1520798{col 82}{space 3} .2309359
{txt}{space 22}12 3  {c |}{col 29}{res}{space 2} .0867548{col 41}{space 2} .0894928{col 52}{space 1}    0.97{col 61}{space 3}0.332{col 69}{space 4}-.0886551{col 82}{space 3} .2621648
{txt}{space 22}12 4  {c |}{col 29}{res}{space 2}-.0131293{col 41}{space 2} .0881265{col 52}{space 1}   -0.15{col 61}{space 3}0.882{col 69}{space 4}-.1858611{col 82}{space 3} .1596025
{txt}{space 22}12 5  {c |}{col 29}{res}{space 2}-.0109358{col 41}{space 2} .0875267{col 52}{space 1}   -0.12{col 61}{space 3}0.901{col 69}{space 4}-.1824919{col 82}{space 3} .1606204
{txt}{space 22}12 6  {c |}{col 29}{res}{space 2}-.0278913{col 41}{space 2} .0858315{col 52}{space 1}   -0.32{col 61}{space 3}0.745{col 69}{space 4}-.1961248{col 82}{space 3} .1403423
{txt}{space 22}12 7  {c |}{col 29}{res}{space 2} .0548345{col 41}{space 2} .0863254{col 52}{space 1}    0.64{col 61}{space 3}0.525{col 69}{space 4}-.1143671{col 82}{space 3} .2240361
{txt}{space 22}12 8  {c |}{col 29}{res}{space 2}-.0759034{col 41}{space 2} .0878178{col 52}{space 1}   -0.86{col 61}{space 3}0.387{col 69}{space 4}-.2480302{col 82}{space 3} .0962235
{txt}{space 22}12 9  {c |}{col 29}{res}{space 2}  .037928{col 41}{space 2} .0864977{col 52}{space 1}    0.44{col 61}{space 3}0.661{col 69}{space 4}-.1316114{col 82}{space 3} .2074673
{txt}{space 22}13 1  {c |}{col 29}{res}{space 2} .0408658{col 41}{space 2} .0868698{col 52}{space 1}    0.47{col 61}{space 3}0.638{col 69}{space 4} -.129403{col 82}{space 3} .2111346
{txt}{space 21}13 10  {c |}{col 29}{res}{space 2} .1885116{col 41}{space 2} .0888044{col 52}{space 1}    2.12{col 61}{space 3}0.034{col 69}{space 4} .0144511{col 82}{space 3} .3625722
{txt}{space 21}13 11  {c |}{col 29}{res}{space 2} .1382177{col 41}{space 2} .1005781{col 52}{space 1}    1.37{col 61}{space 3}0.169{col 69}{space 4}-.0589199{col 82}{space 3} .3353553
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{txt}{space 22}6 45  {c |}{col 29}{res}{space 2} .0224006{col 41}{space 2}   .08681{col 52}{space 1}    0.26{col 61}{space 3}0.796{col 69}{space 4}-.1477509{col 82}{space 3} .1925521
{txt}{space 22}6 46  {c |}{col 29}{res}{space 2} .2006295{col 41}{space 2} .0957952{col 52}{space 1}    2.09{col 61}{space 3}0.036{col 69}{space 4} .0128665{col 82}{space 3} .3883924
{txt}{space 22}6 47  {c |}{col 29}{res}{space 2} .0769052{col 41}{space 2} .0952414{col 52}{space 1}    0.81{col 61}{space 3}0.419{col 69}{space 4}-.1097722{col 82}{space 3} .2635826
{txt}{space 22}6 48  {c |}{col 29}{res}{space 2} .2145719{col 41}{space 2} .0951297{col 52}{space 1}    2.26{col 61}{space 3}0.024{col 69}{space 4} .0281136{col 82}{space 3} .4010303
{txt}{space 22}6 49  {c |}{col 29}{res}{space 2} .1436392{col 41}{space 2} .0950533{col 52}{space 1}    1.51{col 61}{space 3}0.131{col 69}{space 4}-.0426696{col 82}{space 3} .3299479
{txt}{space 23}6 5  {c |}{col 29}{res}{space 2} .0667453{col 41}{space 2} .0882467{col 52}{space 1}    0.76{col 61}{space 3}0.449{col 69}{space 4}-.1062221{col 82}{space 3} .2397127
{txt}{space 22}6 50  {c |}{col 29}{res}{space 2} .0906798{col 41}{space 2} .0950378{col 52}{space 1}    0.95{col 61}{space 3}0.340{col 69}{space 4}-.0955985{col 82}{space 3}  .276958
{txt}{space 22}6 51  {c |}{col 29}{res}{space 2} .2228405{col 41}{space 2} .0942689{col 52}{space 1}    2.36{col 61}{space 3}0.018{col 69}{space 4} .0380692{col 82}{space 3} .4076118
{txt}{space 22}6 52  {c |}{col 29}{res}{space 2} .0588527{col 41}{space 2} .0924092{col 52}{space 1}    0.64{col 61}{space 3}0.524{col 69}{space 4}-.1222735{col 82}{space 3} .2399789
{txt}{space 23}6 6  {c |}{col 29}{res}{space 2} .0059152{col 41}{space 2} .0915976{col 52}{space 1}    0.06{col 61}{space 3}0.949{col 69}{space 4}-.1736202{col 82}{space 3} .1854506
{txt}{space 23}6 7  {c |}{col 29}{res}{space 2} .1073275{col 41}{space 2} .0883938{col 52}{space 1}    1.21{col 61}{space 3}0.225{col 69}{space 4}-.0659284{col 82}{space 3} .2805833
{txt}{space 23}6 8  {c |}{col 29}{res}{space 2}  .053995{col 41}{space 2}  .091348{col 52}{space 1}    0.59{col 61}{space 3}0.554{col 69}{space 4}-.1250511{col 82}{space 3} .2330411
{txt}{space 23}6 9  {c |}{col 29}{res}{space 2} .1405001{col 41}{space 2} .0909228{col 52}{space 1}    1.55{col 61}{space 3}0.122{col 69}{space 4}-.0377127{col 82}{space 3} .3187129
{txt}{space 23}8 1  {c |}{col 29}{res}{space 2} .0533355{col 41}{space 2} .0897093{col 52}{space 1}    0.59{col 61}{space 3}0.552{col 69}{space 4}-.1224986{col 82}{space 3} .2291697
{txt}{space 23}8 2  {c |}{col 29}{res}{space 2}  .150826{col 41}{space 2}  .087785{col 52}{space 1}    1.72{col 61}{space 3}0.086{col 69}{space 4}-.0212365{col 82}{space 3} .3228884
{txt}{space 23}8 3  {c |}{col 29}{res}{space 2} .0624968{col 41}{space 2} .0868088{col 52}{space 1}    0.72{col 61}{space 3}0.472{col 69}{space 4}-.1076523{col 82}{space 3}  .232646
{txt}{space 23}8 4  {c |}{col 29}{res}{space 2}-.0081383{col 41}{space 2} .0853461{col 52}{space 1}   -0.10{col 61}{space 3}0.924{col 69}{space 4}-.1754204{col 82}{space 3} .1591438
{txt}{space 23}8 5  {c |}{col 29}{res}{space 2}-.0518707{col 41}{space 2} .0867845{col 52}{space 1}   -0.60{col 61}{space 3}0.550{col 69}{space 4}-.2219723{col 82}{space 3} .1182308
{txt}{space 23}8 6  {c |}{col 29}{res}{space 2}-.0413631{col 41}{space 2} .0873094{col 52}{space 1}   -0.47{col 61}{space 3}0.636{col 69}{space 4}-.2124934{col 82}{space 3} .1297672
{txt}{space 23}9 1  {c |}{col 29}{res}{space 2} .0622898{col 41}{space 2}  .089365{col 52}{space 1}    0.70{col 61}{space 3}0.486{col 69}{space 4}-.1128697{col 82}{space 3} .2374492
{txt}{space 23}9 2  {c |}{col 29}{res}{space 2} .2248371{col 41}{space 2} .0950078{col 52}{space 1}    2.37{col 61}{space 3}0.018{col 69}{space 4} .0386174{col 82}{space 3} .4110567
{txt}{space 23}9 3  {c |}{col 29}{res}{space 2} .0539102{col 41}{space 2} .0890712{col 52}{space 1}    0.61{col 61}{space 3}0.545{col 69}{space 4}-.1206733{col 82}{space 3} .2284938
{txt}{space 23}9 4  {c |}{col 29}{res}{space 2} .1164046{col 41}{space 2} .0876064{col 52}{space 1}    1.33{col 61}{space 3}0.184{col 69}{space 4}-.0553078{col 82}{space 3}  .288117
{txt}{space 23}9 5  {c |}{col 29}{res}{space 2} .2667669{col 41}{space 2} .0918446{col 52}{space 1}    2.90{col 61}{space 3}0.004{col 69}{space 4} .0867474{col 82}{space 3} .4467864
{txt}{space 23}9 6  {c |}{col 29}{res}{space 2} .0839432{col 41}{space 2} .0887276{col 52}{space 1}    0.95{col 61}{space 3}0.344{col 69}{space 4}-.0899669{col 82}{space 3} .2578533
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} .1293927{col 41}{space 2} .0681206{col 52}{space 1}    1.90{col 61}{space 3}0.058{col 69}{space 4}-.0041266{col 82}{space 3} .2629121
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. 
. **************************************
. **************************************
. ****** Table 2 (Logit with RE) *******
. **************************************
. **************************************
. 
. eststo prore2lib, title("Model I"):  melogit supportall2 i.protest  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy  if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    29,882
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       453

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        13
{col 63}{txt}avg{col 67}={res}{col 69}      66.0
{col 63}{txt}max{col 67}={res}{col 69}       137

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={res}{col 70}   420.74
{txt}Log likelihood = {res}-20262.391{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} supportall2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12} {c |}
{space 5}protest {c |}
{space 4}Protest  {c |}{col 14}{res}{space 2} .2928943{col 26}{space 2} .0543016{col 37}{space 1}    5.39{col 46}{space 3}0.000{col 54}{space 4}  .186465{col 67}{space 3} .3993235
{txt}{space 8}educ {c |}{col 14}{res}{space 2} .1702257{col 26}{space 2} .1035949{col 37}{space 1}    1.64{col 46}{space 3}0.100{col 54}{space 4}-.0328166{col 67}{space 3} .3732679
{txt}{space 9}dem {c |}{col 14}{res}{space 2} .5303274{col 26}{space 2} .0313564{col 37}{space 1}   16.91{col 46}{space 3}0.000{col 54}{space 4} .4688699{col 67}{space 3} .5917849
{txt}{space 1}lengthserve {c |}{col 14}{res}{space 2}-.0951149{col 26}{space 2} .0895483{col 37}{space 1}   -1.06{col 46}{space 3}0.288{col 54}{space 4}-.2706263{col 67}{space 3} .0803965
{txt}{space 2}votemargin {c |}{col 14}{res}{space 2} .3497326{col 26}{space 2} .1106224{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4} .1329167{col 67}{space 3} .5665485
{txt}{space 6}blkrep {c |}{col 14}{res}{space 2}-.0950337{col 26}{space 2} .0576596{col 37}{space 1}   -1.65{col 46}{space 3}0.099{col 54}{space 4}-.2080445{col 67}{space 3} .0179771
{txt}{space 5}hisprep {c |}{col 14}{res}{space 2} .1282763{col 26}{space 2} .0805157{col 37}{space 1}    1.59{col 46}{space 3}0.111{col 54}{space 4}-.0295316{col 67}{space 3} .2860843
{txt}{space 4}womanrep {c |}{col 14}{res}{space 2} .1582131{col 26}{space 2} .0498324{col 37}{space 1}    3.17{col 46}{space 3}0.001{col 54}{space 4} .0605434{col 67}{space 3} .2558829
{txt}{space 12} {c |}
{space 5}cnrptyy {c |}
{space 8}103  {c |}{col 14}{res}{space 2} .1232545{col 26}{space 2} .0312753{col 37}{space 1}    3.94{col 46}{space 3}0.000{col 54}{space 4} .0619562{col 67}{space 3} .1845529
{txt}{space 8}104  {c |}{col 14}{res}{space 2}  .077796{col 26}{space 2} .0335601{col 37}{space 1}    2.32{col 46}{space 3}0.020{col 54}{space 4} .0120195{col 67}{space 3} .1435726
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.8689029{col 26}{space 2}   .08855{col 37}{space 1}   -9.81{col 46}{space 3}0.000{col 54}{space 4}-1.042458{col 67}{space 3}-.6953481
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .0448988{col 26}{space 2} .0077452{col 54}{space 4} .0320183{col 67}{space 3} .0629607
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 69.58{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. eststo pro2rediff: margins, dydx(protest) vsquish atmeans post
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}    29,882
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.protest}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:0.protest}{space 7}{txt:=} {space 3}.9229971 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.protest}{space 7}{txt:=} {space 3}.0770029 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3708415 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 4}.568804 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1676775 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.6604073 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 4}.084064 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.0386855 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1002945 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.2623988 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.4398635 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2977378 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}protest {c |}
{space 4}Protest  {c |}{col 14}{res}{space 2} .0722467{col 26}{space 2} .0133688{col 37}{space 1}    5.40{col 46}{space 3}0.000{col 54}{space 4} .0460443{col 67}{space 3} .0984491
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. **** Figure 2, Model I ****
. marginsplot, xlabel(1 "Protest" ) graphregion(fcolor(white)) legend(off)  ///
> plotopts(color(gray) msize(small) lcolor(black) ) recast(bar) ///
> ciopts(lcolor(black) lpattern(dash)) ylabel(0(.2)1)  ///
> ytitle("Difference in Probability of Support") title ("Model I", color(black)) xtitle (" ")

{text}{p 2 6 2}Variables that uniquely identify margins: {p_end}
{res}{txt}
{com}. 
. 
. 
. eststo pre2lib, title("Model II"):  melogit supportall2 i.poorhc educ dem lengthserve votemargin blkrep hisprep womanrep i.cnrptyy  if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    29,882
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       453

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        13
{col 63}{txt}avg{col 67}={res}{col 69}      66.0
{col 63}{txt}max{col 67}={res}{col 69}       137

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   426.90
{txt}Log likelihood = {res}-20256.582{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 supportall2{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 28} {c |}
{space 22}poorhc {c |}
{space 1}Higher Income Participants  {c |}{col 30}{res}{space 2} .2820792{col 42}{space 2} .0544773{col 53}{space 1}    5.18{col 62}{space 3}0.000{col 70}{space 4} .1753056{col 83}{space 3} .3888528
{txt}{space 4}Low-Income Participants  {c |}{col 30}{res}{space 2} 1.883899{col 42}{space 2} .5482357{col 53}{space 1}    3.44{col 62}{space 3}0.001{col 70}{space 4} .8093769{col 83}{space 3} 2.958421
{txt}{space 28} {c |}
{space 24}educ {c |}{col 30}{res}{space 2} .1695239{col 42}{space 2} .1037669{col 53}{space 1}    1.63{col 62}{space 3}0.102{col 70}{space 4}-.0338554{col 83}{space 3} .3729032
{txt}{space 25}dem {c |}{col 30}{res}{space 2} .5300756{col 42}{space 2} .0313947{col 53}{space 1}   16.88{col 62}{space 3}0.000{col 70}{space 4} .4685431{col 83}{space 3} .5916082
{txt}{space 17}lengthserve {c |}{col 30}{res}{space 2}-.0939462{col 42}{space 2} .0896436{col 53}{space 1}   -1.05{col 62}{space 3}0.295{col 70}{space 4}-.2696444{col 83}{space 3} .0817521
{txt}{space 18}votemargin {c |}{col 30}{res}{space 2} .3502199{col 42}{space 2}  .110737{col 53}{space 1}    3.16{col 62}{space 3}0.002{col 70}{space 4} .1331794{col 83}{space 3} .5672604
{txt}{space 22}blkrep {c |}{col 30}{res}{space 2}-.0953413{col 42}{space 2} .0577456{col 53}{space 1}   -1.65{col 62}{space 3}0.099{col 70}{space 4}-.2085206{col 83}{space 3} .0178381
{txt}{space 21}hisprep {c |}{col 30}{res}{space 2} .1282645{col 42}{space 2} .0806538{col 53}{space 1}    1.59{col 62}{space 3}0.112{col 70}{space 4} -.029814{col 83}{space 3}  .286343
{txt}{space 20}womanrep {c |}{col 30}{res}{space 2} .1585816{col 42}{space 2} .0499011{col 53}{space 1}    3.18{col 62}{space 3}0.001{col 70}{space 4} .0607773{col 83}{space 3} .2563858
{txt}{space 28} {c |}
{space 21}cnrptyy {c |}
{space 24}103  {c |}{col 30}{res}{space 2} .1240051{col 42}{space 2} .0312904{col 53}{space 1}    3.96{col 62}{space 3}0.000{col 70}{space 4} .0626771{col 83}{space 3} .1853332
{txt}{space 24}104  {c |}{col 30}{res}{space 2} .0795082{col 42}{space 2} .0335722{col 53}{space 1}    2.37{col 62}{space 3}0.018{col 70}{space 4}  .013708{col 83}{space 3} .1453084
{txt}{space 28} {c |}
{space 23}_cons {c |}{col 30}{res}{space 2}-.8700015{col 42}{space 2} .0886514{col 53}{space 1}   -9.81{col 62}{space 3}0.000{col 70}{space 4}-1.043755{col 83}{space 3}-.6962479
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                     {col 30}{c |}
                   var(_cons){c |}{col 30}{res}{space 2} .0452625{col 42}{space 2} .0077736{col 70}{space 4} .0323257{col 83}{space 3} .0633765
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 70.46{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. eststo p2rediff: margins, dydx(poorhc) vsquish atmeans post
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}    29,882
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.poorhc 2.poorhc}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:0.poorhc}{space 8}{txt:=} {space 3}.9229971 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorhc}{space 8}{txt:=} {space 3}.0761328 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.poorhc}{space 8}{txt:=} {space 3}.0008701 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3708415 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 4}.568804 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1676775 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.6604073 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 4}.084064 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.0386855 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1002945 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.2623988 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.4398635 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2977378 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41} Delta-method
{col 29}{c |}      dy/dx{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}poorhc {c |}
Higher Income Participants  {c |}{col 29}{res}{space 2} .0695714{col 41}{space 2} .0134172{col 52}{space 1}    5.19{col 61}{space 3}0.000{col 69}{space 4} .0432742{col 82}{space 3} .0958685
{txt}{space 3}Low-Income Participants  {c |}{col 29}{res}{space 2} .3905589{col 41}{space 2} .0727307{col 52}{space 1}    5.37{col 61}{space 3}0.000{col 69}{space 4} .2480093{col 82}{space 3} .5331085
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 93}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. test 1.poorhc - 2.poorhc = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.poorhc - 2.poorhc = 0{p_end}

{txt}{col 12}chi2(  1) ={res}   19.24
{txt}{col 10}Prob > chi2 =  {res}  0.0000
{txt}
{com}. 
. **** Figure 2, Model II ****
. est restore p2rediff
{txt}(results {stata estimates replay p2rediff:p2rediff} are active now)

{com}. marginsplot, xlabel(1 "Higher Income Participants" 2 "Low-Income Participants") graphregion(fcolor(white)) legend(off)  ///
> plotopts(color(gray) msize(small) lcolor(black) ) recast(bar) ///
> ciopts(lcolor(black) lpattern(dash)) ylabel(0(.2)1)  ///
> ytitle("Difference in Probability of Support") title ("Model II", color(black)) xtitle (" ")

{text}{p 2 6 2}Variables that uniquely identify margins: _deriv{p_end}
{res}{txt}
{com}. 
. 
. eststo nre2lib, title("Model III"):  melogit supportall2 i.nonwhitec educ dem lengthserve votemargin blkrep hisprep womanrep i.cnrptyy  if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    29,882
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       453

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        13
{col 63}{txt}avg{col 67}={res}{col 69}      66.0
{col 63}{txt}max{col 67}={res}{col 69}       137

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   435.87
{txt}Log likelihood = {res}-20255.498{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            supportall2{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23} {c |}
{space 14}nonwhitec {c |}
{space 4}White Participants  {c |}{col 25}{res}{space 2} .2037118{col 37}{space 2} .0591551{col 48}{space 1}    3.44{col 57}{space 3}0.001{col 65}{space 4} .0877698{col 78}{space 3} .3196537
{txt}{space 1}Nonwhite Participants  {c |}{col 25}{res}{space 2} .6032462{col 37}{space 2} .1007499{col 48}{space 1}    5.99{col 57}{space 3}0.000{col 65}{space 4} .4057801{col 78}{space 3} .8007123
{txt}{space 23} {c |}
{space 19}educ {c |}{col 25}{res}{space 2} .1673735{col 37}{space 2} .1032766{col 48}{space 1}    1.62{col 57}{space 3}0.105{col 65}{space 4} -.035045{col 78}{space 3} .3697919
{txt}{space 20}dem {c |}{col 25}{res}{space 2} .5300698{col 37}{space 2} .0312608{col 48}{space 1}   16.96{col 57}{space 3}0.000{col 65}{space 4} .4687997{col 78}{space 3} .5913399
{txt}{space 12}lengthserve {c |}{col 25}{res}{space 2}-.0971755{col 37}{space 2} .0893103{col 48}{space 1}   -1.09{col 57}{space 3}0.277{col 65}{space 4}-.2722206{col 78}{space 3} .0778695
{txt}{space 13}votemargin {c |}{col 25}{res}{space 2} .3494385{col 37}{space 2} .1103346{col 48}{space 1}    3.17{col 57}{space 3}0.002{col 65}{space 4} .1331866{col 78}{space 3} .5656903
{txt}{space 17}blkrep {c |}{col 25}{res}{space 2}-.0942219{col 37}{space 2} .0574863{col 48}{space 1}   -1.64{col 57}{space 3}0.101{col 65}{space 4} -.206893{col 78}{space 3} .0184493
{txt}{space 16}hisprep {c |}{col 25}{res}{space 2} .1219275{col 37}{space 2} .0802668{col 48}{space 1}    1.52{col 57}{space 3}0.129{col 65}{space 4}-.0353925{col 78}{space 3} .2792474
{txt}{space 15}womanrep {c |}{col 25}{res}{space 2} .1554682{col 37}{space 2} .0496881{col 48}{space 1}    3.13{col 57}{space 3}0.002{col 65}{space 4} .0580814{col 78}{space 3}  .252855
{txt}{space 23} {c |}
{space 16}cnrptyy {c |}
{space 19}103  {c |}{col 25}{res}{space 2} .1203422{col 37}{space 2} .0312702{col 48}{space 1}    3.85{col 57}{space 3}0.000{col 65}{space 4} .0590537{col 78}{space 3} .1816306
{txt}{space 19}104  {c |}{col 25}{res}{space 2} .0695054{col 37}{space 2} .0336267{col 48}{space 1}    2.07{col 57}{space 3}0.039{col 65}{space 4} .0035983{col 78}{space 3} .1354124
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-.8626146{col 37}{space 2} .0883035{col 48}{space 1}   -9.77{col 57}{space 3}0.000{col 65}{space 4}-1.035686{col 78}{space 3} -.689543
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                {col 25}{c |}
              var(_cons){c |}{col 25}{res}{space 2} .0438494{col 37}{space 2} .0076812{col 65}{space 4} .0311068{col 78}{space 3} .0618118
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 66.50{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. eststo n2rediff: margins, dydx(nonwhite) vsquish atmeans post 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}    29,882
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.nonwhitec 2.nonwhitec}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:0.nonwhitec}{space 5}{txt:=} {space 3}.9229971 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhitec}{space 5}{txt:=} {space 3}.0612743 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.nonwhitec}{space 5}{txt:=} {space 3}.0157285 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3708415 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 4}.568804 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1676775 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.6604073 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 4}.084064 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.0386855 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1002945 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.2623988 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.4398635 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2977378 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}      dy/dx{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}nonwhitec {c |}
{space 3}White Participants  {c |}{col 24}{res}{space 2} .0502259{col 36}{space 2}  .014608{col 47}{space 1}    3.44{col 56}{space 3}0.001{col 64}{space 4} .0215948{col 77}{space 3}  .078857
{txt}Nonwhite Participants  {c |}{col 24}{res}{space 2} .1477352{col 36}{space 2} .0239532{col 47}{space 1}    6.17{col 56}{space 3}0.000{col 64}{space 4} .1007877{col 77}{space 3} .1946826
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 88}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. test 1.nonwhitec - 2.nonwhitec = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.nonwhitec - 2.nonwhitec = 0{p_end}

{txt}{col 12}chi2(  1) ={res}   14.03
{txt}{col 10}Prob > chi2 =  {res}  0.0002
{txt}
{com}. 
. **** Figure 2, Model III ****
. est restore n2rediff
{txt}(results {stata estimates replay n2rediff:n2rediff} are active now)

{com}. marginsplot, xlabel(1 "White Participants" 2 "Nonwhite Participants") graphregion(fcolor(white)) legend(off)  ///
> plotopts(color(gray) msize(small) lcolor(black) ) recast(bar) ///
> ciopts(lcolor(black) lpattern(dash)) ylabel(0(.2)1)  ///
> ytitle("Difference in Probability of Support") title ("Model III", color(black)) xtitle (" ")

{text}{p 2 6 2}Variables that uniquely identify margins: _deriv{p_end}
{res}{txt}
{com}. 
. 
. eststo nigre2lib, title("Model IV"):  melogit supportall2 i.noorgigc educ dem lengthserve votemargin blkrep hisprep womanrep i.cnrptyy  if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    29,866
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       453

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        13
{col 63}{txt}avg{col 67}={res}{col 69}      65.9
{col 63}{txt}max{col 67}={res}{col 69}       136

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   418.71
{txt}Log likelihood = {res}-20251.869{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                  supportall2{col 31}{c |}      Coef.{col 43}   Std. Err.{col 55}      z{col 63}   P>|z|{col 71}     [95% Con{col 84}f. Interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29} {c |}
{space 21}noorgigc {c |}
{space 4}Organized Interest Group  {c |}{col 31}{res}{space 2} .2341517{col 43}{space 2} .0771884{col 54}{space 1}    3.03{col 63}{space 3}0.002{col 71}{space 4} .0828652{col 84}{space 3} .3854382
{txt}{space 1}No Organized Interest Group  {c |}{col 31}{res}{space 2} .3188515{col 43}{space 2} .0633946{col 54}{space 1}    5.03{col 63}{space 3}0.000{col 71}{space 4} .1946004{col 84}{space 3} .4431025
{txt}{space 29} {c |}
{space 25}educ {c |}{col 31}{res}{space 2} .1696496{col 43}{space 2} .1036799{col 54}{space 1}    1.64{col 63}{space 3}0.102{col 71}{space 4}-.0335593{col 84}{space 3} .3728584
{txt}{space 26}dem {c |}{col 31}{res}{space 2}   .53074{col 43}{space 2} .0313728{col 54}{space 1}   16.92{col 63}{space 3}0.000{col 71}{space 4} .4692504{col 84}{space 3} .5922296
{txt}{space 18}lengthserve {c |}{col 31}{res}{space 2}-.0966516{col 43}{space 2} .0895954{col 54}{space 1}   -1.08{col 63}{space 3}0.281{col 71}{space 4}-.2722553{col 84}{space 3} .0789522
{txt}{space 19}votemargin {c |}{col 31}{res}{space 2} .3530516{col 43}{space 2}  .110666{col 54}{space 1}    3.19{col 63}{space 3}0.001{col 71}{space 4} .1361502{col 84}{space 3}  .569953
{txt}{space 23}blkrep {c |}{col 31}{res}{space 2}-.0947494{col 43}{space 2} .0576928{col 54}{space 1}   -1.64{col 63}{space 3}0.101{col 71}{space 4}-.2078253{col 84}{space 3} .0183265
{txt}{space 22}hisprep {c |}{col 31}{res}{space 2} .1262053{col 43}{space 2} .0805832{col 54}{space 1}    1.57{col 63}{space 3}0.117{col 71}{space 4}-.0317349{col 84}{space 3} .2841454
{txt}{space 21}womanrep {c |}{col 31}{res}{space 2}  .157128{col 43}{space 2} .0498624{col 54}{space 1}    3.15{col 63}{space 3}0.002{col 71}{space 4} .0593995{col 84}{space 3} .2548565
{txt}{space 29} {c |}
{space 22}cnrptyy {c |}
{space 25}103  {c |}{col 31}{res}{space 2} .1220391{col 43}{space 2} .0312949{col 54}{space 1}    3.90{col 63}{space 3}0.000{col 71}{space 4} .0607022{col 84}{space 3}  .183376
{txt}{space 25}104  {c |}{col 31}{res}{space 2} .0783494{col 43}{space 2} .0335808{col 54}{space 1}    2.33{col 63}{space 3}0.020{col 71}{space 4} .0125323{col 84}{space 3} .1441665
{txt}{space 29} {c |}
{space 24}_cons {c |}{col 31}{res}{space 2}-.8703408{col 43}{space 2} .0885831{col 54}{space 1}   -9.83{col 63}{space 3}0.000{col 71}{space 4} -1.04396{col 84}{space 3}-.6967212
{txt}{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                      {col 31}{c |}
                    var(_cons){c |}{col 31}{res}{space 2} .0449979{col 43}{space 2} .0077578{col 71}{space 4} .0320953{col 84}{space 3} .0630875
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 69.67{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. eststo ig2rediff: margins, dydx(noorgig) vsquish atmeans post
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}    29,866
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.noorgigc 2.noorgigc}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:0.noorgigc}{space 6}{txt:=} {space 3}.9234916 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgigc}{space 6}{txt:=} {space 3}.0298333 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.noorgigc}{space 6}{txt:=} {space 3}.0466751 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3707917 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.5686734 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1676896 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.6603619 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.0839751 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.0386393 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1002143 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.2625059 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.4395969 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2978973 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42} Delta-method
{col 30}{c |}      dy/dx{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}noorgigc {c |}
{space 3}Organized Interest Group  {c |}{col 30}{res}{space 2} .0577363{col 42}{space 2} .0190591{col 53}{space 1}    3.03{col 62}{space 3}0.002{col 70}{space 4} .0203812{col 83}{space 3} .0950914
{txt}No Organized Interest Group  {c |}{col 30}{res}{space 2} .0786448{col 42}{space 2}  .015596{col 53}{space 1}    5.04{col 62}{space 3}0.000{col 70}{space 4} .0480773{col 83}{space 3} .1092124
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 94}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. test 1.noorgigc - 2.noorgigc = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.noorgigc - 2.noorgigc = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.94
{txt}{col 10}Prob > chi2 =  {res}  0.3319
{txt}
{com}. 
. **** Figure 2, Model IV ****
. est restore ig2rediff
{txt}(results {stata estimates replay ig2rediff:ig2rediff} are active now)

{com}. marginsplot, xlabel(1 "Organized Interest Group" 2 "No Organized Interest Group") graphregion(fcolor(white)) legend(off)  ///
> plotopts(color(gray) msize(small) lcolor(black) ) recast(bar) ///
> ciopts(lcolor(black) lpattern(dash)) ylabel(0(.2)1)  ///
> ytitle("Difference in Probability of Support") title ("Model IV", color(black)) xtitle (" ")

{text}{p 2 6 2}Variables that uniquely identify margins: _deriv{p_end}
{res}{txt}
{com}. 
. 
. 
. 
. **** Table 2**** 
. **** Table 2**** 
. **** Table 2**** 
. **** Table 2**** 
. 
. esttab prore2lib pre2lib nre2lib nigre2lib using example.tex, booktabs scalars("N_g Groups" "ll Log likelihood" "lrtest_chi2 LR test Chi$^2$") label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars ///
>                  title(Legislative Support and Constituency Preferences  (Liberal Only)  \label{c -(}tabmain{c )-}) ///
>                  mtitles("Model I" "Model II" "Model III" "Model IV") ///
>                  indicate("Congress Year Dummies = *cnrptyy" ) ///
>                  addnotes("Coefficients are Log Odds. All independent variables are coded 0 to 1.") ///
>                  order(1.protest 1.poorhc 2.poorhc 1.nonwhitec 2.nonwhitec 1.noorgigc 2.noorgigc educ dem lengthserve votemargin blkrep hisprep womanrep) ///
>                  star(* 0.10 ** 0.05 *** 0.01) nobaselevels nodepvars
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
. 
. ****************************
. ****************************
. ****** Placebo Tests *******
. ****************************
. ****************************
. 
. *** Min RCVs ***
. *** Min RCVs ***
. *** Min RCVs ***
. 
. eststo promin2, title("Model I"): mixed wnommin10to1 protest   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}    33,929
{txt}Group variable: {res}statecd{col 49}{txt}Number of groups{col 67}={col 69}{res}       453

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        18
{txt}{col 63}avg{col 67}={col 69}{res}      74.9
{txt}{col 63}max{col 67}={col 69}{res}       219

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res} 67694.33
{txt}Log likelihood = {res} 37185.589{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}wnommin10to1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}protest {c |}{col 14}{res}{space 2} .0133104{col 26}{space 2} .0019731{col 37}{space 1}    6.75{col 46}{space 3}0.000{col 54}{space 4} .0094431{col 67}{space 3} .0171777
{txt}{space 8}educ {c |}{col 14}{res}{space 2} .0590641{col 26}{space 2} .0067883{col 37}{space 1}    8.70{col 46}{space 3}0.000{col 54}{space 4} .0457594{col 67}{space 3} .0723689
{txt}{space 9}dem {c |}{col 14}{res}{space 2} .3890577{col 26}{space 2} .0018874{col 37}{space 1}  206.13{col 46}{space 3}0.000{col 54}{space 4} .3853584{col 67}{space 3}  .392757
{txt}{space 1}lengthserve {c |}{col 14}{res}{space 2} .0414329{col 26}{space 2} .0048382{col 37}{space 1}    8.56{col 46}{space 3}0.000{col 54}{space 4} .0319503{col 67}{space 3} .0509155
{txt}{space 2}votemargin {c |}{col 14}{res}{space 2} .1562595{col 26}{space 2} .0048861{col 37}{space 1}   31.98{col 46}{space 3}0.000{col 54}{space 4} .1466829{col 67}{space 3} .1658361
{txt}{space 6}blkrep {c |}{col 14}{res}{space 2} .1386822{col 26}{space 2} .0037272{col 37}{space 1}   37.21{col 46}{space 3}0.000{col 54}{space 4} .1313771{col 67}{space 3} .1459873
{txt}{space 5}hisprep {c |}{col 14}{res}{space 2} .1178785{col 26}{space 2} .0055711{col 37}{space 1}   21.16{col 46}{space 3}0.000{col 54}{space 4} .1069593{col 67}{space 3} .1287976
{txt}{space 4}womanrep {c |}{col 14}{res}{space 2}-.0170373{col 26}{space 2} .0029663{col 37}{space 1}   -5.74{col 46}{space 3}0.000{col 54}{space 4}-.0228511{col 67}{space 3}-.0112236
{txt}{space 12} {c |}
{space 5}cnrptyy {c |}
{space 8}103  {c |}{col 14}{res}{space 2}-.0293417{col 26}{space 2} .0012542{col 37}{space 1}  -23.40{col 46}{space 3}0.000{col 54}{space 4}-.0317998{col 67}{space 3}-.0268835
{txt}{space 8}104  {c |}{col 14}{res}{space 2} .1215601{col 26}{space 2} .0013456{col 37}{space 1}   90.34{col 46}{space 3}0.000{col 54}{space 4} .1189227{col 67}{space 3} .1241974
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .1239496{col 26}{space 2} .0066729{col 37}{space 1}   18.58{col 46}{space 3}0.000{col 54}{space 4}  .110871{col 67}{space 3} .1370282
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}statecd{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33}  .012109{col 44} .0008144{col 58} .0106136{col 70} .0138152
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  .006122{col 44} .0000473{col 58} .0060299{col 70} .0062154
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}31186.21{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. eststo pmin2, title("Model II"): mixed wnommin10to1 i.poorhc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}    33,929
{txt}Group variable: {res}statecd{col 49}{txt}Number of groups{col 67}={col 69}{res}       453

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        18
{txt}{col 63}avg{col 67}={col 69}{res}      74.9
{txt}{col 63}max{col 67}={col 69}{res}       219

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res} 67700.40
{txt}Log likelihood = {res} 37186.605{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               wnommin10to1{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}poorhc {c |}
Higher Income Participants  {c |}{col 29}{res}{space 2} .0131566{col 41}{space 2}  .001976{col 52}{space 1}    6.66{col 61}{space 3}0.000{col 69}{space 4} .0092836{col 82}{space 3} .0170295
{txt}{space 3}Low-Income Participants  {c |}{col 29}{res}{space 2} .0305178{col 41}{space 2} .0122283{col 52}{space 1}    2.50{col 61}{space 3}0.013{col 69}{space 4} .0065508{col 82}{space 3} .0544849
{txt}{space 27} {c |}
{space 23}educ {c |}{col 29}{res}{space 2} .0590548{col 41}{space 2} .0067881{col 52}{space 1}    8.70{col 61}{space 3}0.000{col 69}{space 4} .0457504{col 82}{space 3} .0723592
{txt}{space 24}dem {c |}{col 29}{res}{space 2} .3890592{col 41}{space 2} .0018874{col 52}{space 1}  206.14{col 61}{space 3}0.000{col 69}{space 4}   .38536{col 82}{space 3} .3927584
{txt}{space 16}lengthserve {c |}{col 29}{res}{space 2} .0414613{col 41}{space 2} .0048381{col 52}{space 1}    8.57{col 61}{space 3}0.000{col 69}{space 4} .0319789{col 82}{space 3} .0509438
{txt}{space 17}votemargin {c |}{col 29}{res}{space 2} .1562583{col 41}{space 2}  .004886{col 52}{space 1}   31.98{col 61}{space 3}0.000{col 69}{space 4}  .146682{col 82}{space 3} .1658346
{txt}{space 21}blkrep {c |}{col 29}{res}{space 2} .1386864{col 41}{space 2} .0037271{col 52}{space 1}   37.21{col 61}{space 3}0.000{col 69}{space 4} .1313815{col 82}{space 3} .1459913
{txt}{space 20}hisprep {c |}{col 29}{res}{space 2} .1178718{col 41}{space 2} .0055709{col 52}{space 1}   21.16{col 61}{space 3}0.000{col 69}{space 4}  .106953{col 82}{space 3} .1287907
{txt}{space 19}womanrep {c |}{col 29}{res}{space 2}  -.01704{col 41}{space 2} .0029662{col 52}{space 1}   -5.74{col 61}{space 3}0.000{col 69}{space 4}-.0228536{col 82}{space 3}-.0112265
{txt}{space 27} {c |}
{space 20}cnrptyy {c |}
{space 23}103  {c |}{col 29}{res}{space 2}-.0293609{col 41}{space 2} .0012542{col 52}{space 1}  -23.41{col 61}{space 3}0.000{col 69}{space 4}-.0318191{col 82}{space 3}-.0269027
{txt}{space 23}104  {c |}{col 29}{res}{space 2} .1215679{col 41}{space 2} .0013456{col 52}{space 1}   90.35{col 61}{space 3}0.000{col 69}{space 4} .1189307{col 82}{space 3} .1242052
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} .1239544{col 41}{space 2} .0066727{col 52}{space 1}   18.58{col 61}{space 3}0.000{col 69}{space 4}  .110876{col 82}{space 3} .1370327
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}statecd{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0121086{col 44} .0008143{col 58} .0106133{col 70} .0138147
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0061216{col 44} .0000473{col 58} .0060296{col 70} .0062151
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}31185.62{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. test 1.poorhc-2.poorhc=0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[wnommin10to1]1.poorhc - [wnommin10to1]2.poorhc = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    2.03
{txt}{col 10}Prob > chi2 =  {res}  0.1539
{txt}
{com}. 
. eststo nmin2, title("Model III"): mixed wnommin10to1 i.nonwhitec  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}    33,929
{txt}Group variable: {res}statecd{col 49}{txt}Number of groups{col 67}={col 69}{res}       453

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        18
{txt}{col 63}avg{col 67}={col 69}{res}      74.9
{txt}{col 63}max{col 67}={col 69}{res}       219

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res} 67723.17
{txt}Log likelihood = {res}  37190.35{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          wnommin10to1{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}nonwhitec {c |}
{space 3}White Participants  {c |}{col 24}{res}{space 2} .0160081{col 36}{space 2} .0021578{col 47}{space 1}    7.42{col 56}{space 3}0.000{col 64}{space 4} .0117788{col 77}{space 3} .0202373
{txt}Nonwhite Participants  {c |}{col 24}{res}{space 2} .0054824{col 36}{space 2} .0032135{col 47}{space 1}    1.71{col 56}{space 3}0.088{col 64}{space 4}-.0008159{col 77}{space 3} .0117807
{txt}{space 22} {c |}
{space 18}educ {c |}{col 24}{res}{space 2} .0596796{col 36}{space 2} .0067902{col 47}{space 1}    8.79{col 56}{space 3}0.000{col 64}{space 4} .0463711{col 77}{space 3} .0729881
{txt}{space 19}dem {c |}{col 24}{res}{space 2} .3890452{col 36}{space 2} .0018872{col 47}{space 1}  206.15{col 56}{space 3}0.000{col 64}{space 4} .3853464{col 77}{space 3}  .392744
{txt}{space 11}lengthserve {c |}{col 24}{res}{space 2} .0415941{col 36}{space 2} .0048378{col 47}{space 1}    8.60{col 56}{space 3}0.000{col 64}{space 4} .0321122{col 77}{space 3} .0510759
{txt}{space 12}votemargin {c |}{col 24}{res}{space 2} .1563222{col 36}{space 2} .0048855{col 47}{space 1}   32.00{col 56}{space 3}0.000{col 64}{space 4} .1467468{col 77}{space 3} .1658975
{txt}{space 16}blkrep {c |}{col 24}{res}{space 2} .1388364{col 36}{space 2}  .003727{col 47}{space 1}   37.25{col 56}{space 3}0.000{col 64}{space 4} .1315316{col 77}{space 3} .1461411
{txt}{space 15}hisprep {c |}{col 24}{res}{space 2} .1183445{col 36}{space 2} .0055723{col 47}{space 1}   21.24{col 56}{space 3}0.000{col 64}{space 4}  .107423{col 77}{space 3} .1292661
{txt}{space 14}womanrep {c |}{col 24}{res}{space 2} -.016768{col 36}{space 2} .0029671{col 47}{space 1}   -5.65{col 56}{space 3}0.000{col 64}{space 4}-.0225834{col 77}{space 3}-.0109526
{txt}{space 22} {c |}
{space 15}cnrptyy {c |}
{space 18}103  {c |}{col 24}{res}{space 2}-.0292414{col 36}{space 2} .0012544{col 47}{space 1}  -23.31{col 56}{space 3}0.000{col 64}{space 4}-.0317001{col 77}{space 3}-.0267828
{txt}{space 18}104  {c |}{col 24}{res}{space 2}  .121739{col 36}{space 2} .0013467{col 47}{space 1}   90.40{col 56}{space 3}0.000{col 64}{space 4} .1190996{col 77}{space 3} .1243784
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2}   .12351{col 36}{space 2} .0066724{col 47}{space 1}   18.51{col 56}{space 3}0.000{col 64}{space 4} .1104323{col 77}{space 3} .1365877
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}statecd{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0120993{col 44} .0008137{col 58} .0106051{col 70}  .013804
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0061203{col 44} .0000473{col 58} .0060283{col 70} .0062137
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}31175.27{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. test 1.nonwhitec-2.nonwhitec=0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[wnommin10to1]1.nonwhitec - [wnommin10to1]2.nonwhitec = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    9.52
{txt}{col 10}Prob > chi2 =  {res}  0.0020
{txt}
{com}. 
. eststo nigmin2, title("Model IV"): mixed wnommin10to1 i.noorgigc   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog  
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}    33,913
{txt}Group variable: {res}statecd{col 49}{txt}Number of groups{col 67}={col 69}{res}       453

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        18
{txt}{col 63}avg{col 67}={col 69}{res}      74.9
{txt}{col 63}max{col 67}={col 69}{res}       218

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res} 67654.55
{txt}Log likelihood = {res} 37164.011{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                wnommin10to1{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}noorgigc {c |}
{space 3}Organized Interest Group  {c |}{col 30}{res}{space 2} .0092624{col 42}{space 2}  .002663{col 53}{space 1}    3.48{col 62}{space 3}0.001{col 70}{space 4}  .004043{col 83}{space 3} .0144818
{txt}No Organized Interest Group  {c |}{col 30}{res}{space 2} .0158486{col 42}{space 2} .0022581{col 53}{space 1}    7.02{col 62}{space 3}0.000{col 70}{space 4} .0114229{col 83}{space 3} .0202744
{txt}{space 28} {c |}
{space 24}educ {c |}{col 30}{res}{space 2} .0588495{col 42}{space 2} .0067942{col 53}{space 1}    8.66{col 62}{space 3}0.000{col 70}{space 4} .0455331{col 83}{space 3} .0721659
{txt}{space 25}dem {c |}{col 30}{res}{space 2} .3890774{col 42}{space 2} .0018883{col 53}{space 1}  206.05{col 62}{space 3}0.000{col 70}{space 4} .3853764{col 83}{space 3} .3927783
{txt}{space 17}lengthserve {c |}{col 30}{res}{space 2} .0414854{col 42}{space 2} .0048406{col 53}{space 1}    8.57{col 62}{space 3}0.000{col 70}{space 4}  .031998{col 83}{space 3} .0509727
{txt}{space 18}votemargin {c |}{col 30}{res}{space 2} .1561977{col 42}{space 2} .0048876{col 53}{space 1}   31.96{col 62}{space 3}0.000{col 70}{space 4} .1466181{col 83}{space 3} .1657772
{txt}{space 22}blkrep {c |}{col 30}{res}{space 2} .1385763{col 42}{space 2} .0037291{col 53}{space 1}   37.16{col 62}{space 3}0.000{col 70}{space 4} .1312674{col 83}{space 3} .1458852
{txt}{space 21}hisprep {c |}{col 30}{res}{space 2} .1177822{col 42}{space 2} .0055754{col 53}{space 1}   21.13{col 62}{space 3}0.000{col 70}{space 4} .1068547{col 83}{space 3} .1287097
{txt}{space 20}womanrep {c |}{col 30}{res}{space 2}-.0170835{col 42}{space 2} .0029677{col 53}{space 1}   -5.76{col 62}{space 3}0.000{col 70}{space 4}   -.0229{col 83}{space 3} -.011267
{txt}{space 28} {c |}
{space 21}cnrptyy {c |}
{space 24}103  {c |}{col 30}{res}{space 2}-.0293073{col 42}{space 2} .0012548{col 53}{space 1}  -23.36{col 62}{space 3}0.000{col 70}{space 4}-.0317666{col 83}{space 3} -.026848
{txt}{space 24}104  {c |}{col 30}{res}{space 2} .1216313{col 42}{space 2} .0013463{col 53}{space 1}   90.35{col 62}{space 3}0.000{col 70}{space 4} .1189926{col 83}{space 3} .1242699
{txt}{space 28} {c |}
{space 23}_cons {c |}{col 30}{res}{space 2} .1240296{col 42}{space 2}  .006675{col 53}{space 1}   18.58{col 62}{space 3}0.000{col 70}{space 4} .1109468{col 83}{space 3} .1371125
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}statecd{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0121139{col 44} .0008147{col 58} .0106179{col 70} .0138208
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0061232{col 44} .0000473{col 58} .0060312{col 70} .0062167
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}31169.97{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. test 1.noorgigc-2.noorgigc=0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[wnommin10to1]1.noorgigc - [wnommin10to1]2.noorgigc = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    5.23
{txt}{col 10}Prob > chi2 =  {res}  0.0222
{txt}
{com}. 
. *** Appendix E, Table 5 ****
. esttab promin2 pmin2 nmin2 nigmin2 using example.tex, booktabs scalars("N_g Groups" "ll Log likelihood" "lrtest_chi2 LR test Chi$^2$") label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars transform(ln*: exp(2*@) 2*exp(2*@)) ///
>                  title(General Legislative Support (via minority w-nominate scores) and Constituent Preferences  (Liberal Only) \label{c -(}tabplacebomin{c )-}) ///
>                  indicate("Congress Year Dummies = *cnrptyy" ) ///
>                  addnotes("Coefficients are Log Odds. All independent variables are coded 0 to 1.") ///
>                  order(protest 1.poorhc 2.poorhc 1.nonwhitec 2.nonwhitec 1.noorgigc 2.noorgigc educ dem lengthserve votemargin blkrep hisprep womanrep) ///
>                  star(* 0.10 ** 0.05 *** 0.01) nobaselevels nodepvars
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
. *** Figure 3, Minority Roll Call Votes, Model I ***
. coefplot promin2,  keep(protest)  vertical recast(bar)  /// 
>                 ciopts(lcolor(black) recast(rcap)) citop yline(0, lcolor(black) lpattern(dash)) ///
>                 nokey ylabel(-.04(.01).06) xlabel(1 "Protest")  graphregion(fcolor(white) lcolor(white)) ///
>                 title ("Model I", color(black)) xtitle (" ") bcolor(gray)
{res}{txt}
{com}. 
. *** Figure 3, Minority Roll Call Votes, Model II ***
. coefplot pmin2,  keep(1.poorhc 2.poorhc)  vertical recast(bar)  /// 
>                 ciopts(lcolor(black) recast(rcap)) citop yline(0, lcolor(black) lpattern(dash)) ///
>                 nokey ylabel(-.04(.01).06) xlabel(1 "Higher Income Participants" 2 "Low-Income Participants")  graphregion(fcolor(white) lcolor(white)) ///
>                 title ("Model II", color(black)) xtitle (" ") bcolor(gray)
{res}{txt}
{com}. 
. *** Figure 3, Minority Roll Call Votes, Model III ***
. coefplot nmin2,  keep(1.nonwhitec 2.nonwhitec)  vertical recast(bar)  /// 
>                 ciopts(lcolor(black) recast(rcap)) citop yline(0, lcolor(black) lpattern(dash)) ///
>                 nokey ylabel(-.04(.01).06) xlabel(1 "White Participants" 2 "Nonwhite Participants")  graphregion(fcolor(white) lcolor(white)) ///
>                 title ("Model III", color(black)) xtitle (" ") bcolor(gray)
{res}{txt}
{com}. 
. *** Figure 3, Minority Roll Call Votes, Model IV ***
. coefplot nigmin2,  keep(1.noorgigc 2.noorgigc)  vertical recast(bar)  /// 
>                 ciopts(lcolor(black) recast(rcap)) citop yline(0, lcolor(black) lpattern(dash)) ///
>                 nokey ylabel(-.04(.01).06) xlabel(1 "Organized Interest Group" 2 "No Organized Interest Group")  graphregion(fcolor(white) lcolor(white)) ///
>                 title ("Model IV", color(black)) xtitle (" ") bcolor(gray)
{res}{txt}
{com}. 
. 
. 
. **** All RCVs ****
. **** All RCVs ****
. **** All RCVs ****
. 
. eststo pronom2, title("Model I"): mixed wnom10to1 protest   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}    34,001
{txt}Group variable: {res}statecd{col 49}{txt}Number of groups{col 67}={col 69}{res}       453

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        18
{txt}{col 63}avg{col 67}={col 69}{res}      75.1
{txt}{col 63}max{col 67}={col 69}{res}       219

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={col 70}{res}191938.07
{txt}Log likelihood = {res} 49480.512{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   wnom10to1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}protest {c |}{col 14}{res}{space 2} .0046856{col 26}{space 2} .0013741{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .0019925{col 67}{space 3} .0073787
{txt}{space 8}educ {c |}{col 14}{res}{space 2} .0604507{col 26}{space 2} .0047365{col 37}{space 1}   12.76{col 46}{space 3}0.000{col 54}{space 4} .0511674{col 67}{space 3}  .069734
{txt}{space 9}dem {c |}{col 14}{res}{space 2}  .440924{col 26}{space 2}  .001318{col 37}{space 1}  334.54{col 46}{space 3}0.000{col 54}{space 4} .4383408{col 67}{space 3} .4435073
{txt}{space 1}lengthserve {c |}{col 14}{res}{space 2} .1134657{col 26}{space 2} .0033531{col 37}{space 1}   33.84{col 46}{space 3}0.000{col 54}{space 4} .1068937{col 67}{space 3} .1200377
{txt}{space 2}votemargin {c |}{col 14}{res}{space 2} .0066923{col 26}{space 2} .0034059{col 37}{space 1}    1.96{col 46}{space 3}0.049{col 54}{space 4}  .000017{col 67}{space 3} .0133677
{txt}{space 6}blkrep {c |}{col 14}{res}{space 2} .1327955{col 26}{space 2} .0026084{col 37}{space 1}   50.91{col 46}{space 3}0.000{col 54}{space 4} .1276831{col 67}{space 3} .1379078
{txt}{space 5}hisprep {c |}{col 14}{res}{space 2} .1369771{col 26}{space 2} .0038997{col 37}{space 1}   35.12{col 46}{space 3}0.000{col 54}{space 4} .1293338{col 67}{space 3} .1446204
{txt}{space 4}womanrep {c |}{col 14}{res}{space 2} .0066852{col 26}{space 2} .0020714{col 37}{space 1}    3.23{col 46}{space 3}0.001{col 54}{space 4} .0026253{col 67}{space 3}  .010745
{txt}{space 12} {c |}
{space 5}cnrptyy {c |}
{space 8}103  {c |}{col 14}{res}{space 2}-.0482936{col 26}{space 2} .0008737{col 37}{space 1}  -55.28{col 46}{space 3}0.000{col 54}{space 4} -.050006{col 67}{space 3}-.0465813
{txt}{space 8}104  {c |}{col 14}{res}{space 2}-.1187966{col 26}{space 2} .0009377{col 37}{space 1} -126.68{col 46}{space 3}0.000{col 54}{space 4}-.1206345{col 67}{space 3}-.1169586
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .2670249{col 26}{space 2} .0052146{col 37}{space 1}   51.21{col 46}{space 3}0.000{col 54}{space 4} .2568045{col 67}{space 3} .2772454
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}statecd{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0084073{col 44} .0005625{col 58} .0073741{col 70} .0095852
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0029703{col 44} .0000229{col 58} .0029257{col 70} .0030156
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}41146.77{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. eststo pnom2, title("Model II"): mixed wnom10to1 i.poorhc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}    34,001
{txt}Group variable: {res}statecd{col 49}{txt}Number of groups{col 67}={col 69}{res}       453

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        18
{txt}{col 63}avg{col 67}={col 69}{res}      75.1
{txt}{col 63}max{col 67}={col 69}{res}       219

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}191954.15
{txt}Log likelihood = {res} 49481.722{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                  wnom10to1{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}poorhc {c |}
Higher Income Participants  {c |}{col 29}{res}{space 2} .0048023{col 41}{space 2} .0013761{col 52}{space 1}    3.49{col 61}{space 3}0.000{col 69}{space 4} .0021052{col 82}{space 3} .0074993
{txt}{space 3}Low-Income Participants  {c |}{col 29}{res}{space 2} -.008391{col 41}{space 2} .0085177{col 52}{space 1}   -0.99{col 61}{space 3}0.325{col 69}{space 4}-.0250854{col 82}{space 3} .0083034
{txt}{space 27} {c |}
{space 23}educ {c |}{col 29}{res}{space 2} .0604578{col 41}{space 2} .0047363{col 52}{space 1}   12.76{col 61}{space 3}0.000{col 69}{space 4} .0511748{col 82}{space 3} .0697408
{txt}{space 24}dem {c |}{col 29}{res}{space 2} .4409229{col 41}{space 2}  .001318{col 52}{space 1}  334.55{col 61}{space 3}0.000{col 69}{space 4} .4383397{col 82}{space 3}  .443506
{txt}{space 16}lengthserve {c |}{col 29}{res}{space 2} .1134439{col 41}{space 2}  .003353{col 52}{space 1}   33.83{col 61}{space 3}0.000{col 69}{space 4} .1068721{col 82}{space 3} .1200157
{txt}{space 17}votemargin {c |}{col 29}{res}{space 2} .0066934{col 41}{space 2} .0034057{col 52}{space 1}    1.97{col 61}{space 3}0.049{col 69}{space 4} .0000182{col 82}{space 3} .0133685
{txt}{space 21}blkrep {c |}{col 29}{res}{space 2} .1327921{col 41}{space 2} .0026083{col 52}{space 1}   50.91{col 61}{space 3}0.000{col 69}{space 4} .1276799{col 82}{space 3} .1379042
{txt}{space 20}hisprep {c |}{col 29}{res}{space 2} .1369821{col 41}{space 2} .0038996{col 52}{space 1}   35.13{col 61}{space 3}0.000{col 69}{space 4} .1293391{col 82}{space 3} .1446252
{txt}{space 19}womanrep {c |}{col 29}{res}{space 2} .0066871{col 41}{space 2} .0020713{col 52}{space 1}    3.23{col 61}{space 3}0.001{col 69}{space 4} .0026273{col 82}{space 3} .0107468
{txt}{space 27} {c |}
{space 20}cnrptyy {c |}
{space 23}103  {c |}{col 29}{res}{space 2} -.048279{col 41}{space 2} .0008737{col 52}{space 1}  -55.26{col 61}{space 3}0.000{col 69}{space 4}-.0499914{col 82}{space 3}-.0465667
{txt}{space 23}104  {c |}{col 29}{res}{space 2}-.1188025{col 41}{space 2} .0009377{col 52}{space 1} -126.69{col 61}{space 3}0.000{col 69}{space 4}-.1206404{col 82}{space 3}-.1169646
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} .2670213{col 41}{space 2} .0052146{col 52}{space 1}   51.21{col 61}{space 3}0.000{col 69}{space 4} .2568008{col 82}{space 3} .2772418
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}statecd{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0084078{col 44} .0005625{col 58} .0073745{col 70} .0095858
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0029701{col 44} .0000229{col 58} .0029255{col 70} .0030154
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}41149.09{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. test 1.poorhc-2.poorhc=0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[wnom10to1]1.poorhc - [wnom10to1]2.poorhc = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    2.42
{txt}{col 10}Prob > chi2 =  {res}  0.1198
{txt}
{com}. eststo nnom2, title("Model III"): mixed wnom10to1 i.nonwhitec  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}    34,001
{txt}Group variable: {res}statecd{col 49}{txt}Number of groups{col 67}={col 69}{res}       453

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        18
{txt}{col 63}avg{col 67}={col 69}{res}      75.1
{txt}{col 63}max{col 67}={col 69}{res}       219

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}191944.71
{txt}Log likelihood = {res} 49481.012{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}             wnom10to1{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}nonwhitec {c |}
{space 3}White Participants  {c |}{col 24}{res}{space 2} .0052962{col 36}{space 2} .0015036{col 47}{space 1}    3.52{col 56}{space 3}0.000{col 64}{space 4} .0023493{col 77}{space 3} .0082431
{txt}Nonwhite Participants  {c |}{col 24}{res}{space 2} .0029223{col 36}{space 2} .0022353{col 47}{space 1}    1.31{col 56}{space 3}0.191{col 64}{space 4}-.0014589{col 77}{space 3} .0073035
{txt}{space 22} {c |}
{space 18}educ {c |}{col 24}{res}{space 2} .0605951{col 36}{space 2} .0047386{col 47}{space 1}   12.79{col 56}{space 3}0.000{col 64}{space 4} .0513077{col 77}{space 3} .0698826
{txt}{space 19}dem {c |}{col 24}{res}{space 2} .4409211{col 36}{space 2}  .001318{col 47}{space 1}  334.54{col 56}{space 3}0.000{col 64}{space 4} .4383379{col 77}{space 3} .4435043
{txt}{space 11}lengthserve {c |}{col 24}{res}{space 2}  .113503{col 36}{space 2} .0033533{col 47}{space 1}   33.85{col 56}{space 3}0.000{col 64}{space 4} .1069307{col 77}{space 3} .1200753
{txt}{space 12}votemargin {c |}{col 24}{res}{space 2} .0067079{col 36}{space 2} .0034058{col 47}{space 1}    1.97{col 56}{space 3}0.049{col 64}{space 4} .0000326{col 77}{space 3} .0133833
{txt}{space 16}blkrep {c |}{col 24}{res}{space 2}  .132831{col 36}{space 2} .0026086{col 47}{space 1}   50.92{col 56}{space 3}0.000{col 64}{space 4} .1277183{col 77}{space 3} .1379438
{txt}{space 15}hisprep {c |}{col 24}{res}{space 2}  .137084{col 36}{space 2} .0039011{col 47}{space 1}   35.14{col 56}{space 3}0.000{col 64}{space 4} .1294379{col 77}{space 3}   .14473
{txt}{space 14}womanrep {c |}{col 24}{res}{space 2} .0067462{col 36}{space 2} .0020723{col 47}{space 1}    3.26{col 56}{space 3}0.001{col 64}{space 4} .0026846{col 77}{space 3} .0108077
{txt}{space 22} {c |}
{space 15}cnrptyy {c |}
{space 18}103  {c |}{col 24}{res}{space 2}-.0482714{col 36}{space 2} .0008739{col 47}{space 1}  -55.24{col 56}{space 3}0.000{col 64}{space 4}-.0499843{col 77}{space 3}-.0465585
{txt}{space 18}104  {c |}{col 24}{res}{space 2}-.1187562{col 36}{space 2} .0009386{col 47}{space 1} -126.53{col 56}{space 3}0.000{col 64}{space 4}-.1205959{col 77}{space 3}-.1169166
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2} .2669227{col 36}{space 2} .0052153{col 47}{space 1}   51.18{col 56}{space 3}0.000{col 64}{space 4} .2567009{col 77}{space 3} .2771445
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}statecd{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0084059{col 44} .0005624{col 58} .0073729{col 70} .0095837
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0029702{col 44} .0000229{col 58} .0029256{col 70} .0030155
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}41139.53{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. test 1.nonwhitec-2.nonwhitec=0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[wnom10to1]1.nonwhitec - [wnom10to1]2.nonwhitec = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    1.00
{txt}{col 10}Prob > chi2 =  {res}  0.3173
{txt}
{com}. eststo nignom2, title("Model IV"): mixed wnom10to1 i.noorgigc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects ML regression{col 49}Number of obs{col 67}={col 69}{res}    33,985
{txt}Group variable: {res}statecd{col 49}{txt}Number of groups{col 67}={col 69}{res}       453

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        18
{txt}{col 63}avg{col 67}={col 69}{res}      75.0
{txt}{col 63}max{col 67}={col 69}{res}       218

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}191854.26
{txt}Log likelihood = {res} 49452.648{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                   wnom10to1{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}noorgigc {c |}
{space 3}Organized Interest Group  {c |}{col 30}{res}{space 2} .0038897{col 42}{space 2} .0018552{col 53}{space 1}    2.10{col 62}{space 3}0.036{col 70}{space 4} .0002535{col 83}{space 3} .0075259
{txt}No Organized Interest Group  {c |}{col 30}{res}{space 2}  .005335{col 42}{space 2} .0015721{col 53}{space 1}    3.39{col 62}{space 3}0.001{col 70}{space 4} .0022538{col 83}{space 3} .0084161
{txt}{space 28} {c |}
{space 24}educ {c |}{col 30}{res}{space 2} .0603602{col 42}{space 2} .0047407{col 53}{space 1}   12.73{col 62}{space 3}0.000{col 70}{space 4} .0510686{col 83}{space 3} .0696518
{txt}{space 25}dem {c |}{col 30}{res}{space 2} .4409516{col 42}{space 2} .0013186{col 53}{space 1}  334.40{col 62}{space 3}0.000{col 70}{space 4} .4383671{col 83}{space 3} .4435361
{txt}{space 17}lengthserve {c |}{col 30}{res}{space 2} .1134672{col 42}{space 2} .0033548{col 53}{space 1}   33.82{col 62}{space 3}0.000{col 70}{space 4} .1068919{col 83}{space 3} .1200425
{txt}{space 18}votemargin {c |}{col 30}{res}{space 2} .0066807{col 42}{space 2}  .003407{col 53}{space 1}    1.96{col 62}{space 3}0.050{col 70}{space 4} 3.17e-06{col 83}{space 3} .0133582
{txt}{space 22}blkrep {c |}{col 30}{res}{space 2} .1328033{col 42}{space 2} .0026098{col 53}{space 1}   50.89{col 62}{space 3}0.000{col 70}{space 4} .1276882{col 83}{space 3} .1379184
{txt}{space 21}hisprep {c |}{col 30}{res}{space 2} .1369772{col 42}{space 2} .0039028{col 53}{space 1}   35.10{col 62}{space 3}0.000{col 70}{space 4} .1293279{col 83}{space 3} .1446265
{txt}{space 20}womanrep {c |}{col 30}{res}{space 2} .0066835{col 42}{space 2} .0020724{col 53}{space 1}    3.23{col 62}{space 3}0.001{col 70}{space 4} .0026217{col 83}{space 3} .0107454
{txt}{space 28} {c |}
{space 21}cnrptyy {c |}
{space 24}103  {c |}{col 30}{res}{space 2}-.0482484{col 42}{space 2} .0008741{col 53}{space 1}  -55.20{col 62}{space 3}0.000{col 70}{space 4}-.0499616{col 83}{space 3}-.0465352
{txt}{space 24}104  {c |}{col 30}{res}{space 2}-.1187655{col 42}{space 2} .0009382{col 53}{space 1} -126.59{col 62}{space 3}0.000{col 70}{space 4}-.1206044{col 83}{space 3}-.1169266
{txt}{space 28} {c |}
{space 23}_cons {c |}{col 30}{res}{space 2} .2670208{col 42}{space 2} .0052157{col 53}{space 1}   51.20{col 62}{space 3}0.000{col 70}{space 4} .2567982{col 83}{space 3} .2772435
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}statecd{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .0084085{col 44} .0005625{col 58} .0073752{col 70} .0095867
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  .002971{col 44} .0000229{col 58} .0029264{col 70} .0030163
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}41117.10{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}.  test 1.noorgigc-2.noorgigc=0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[wnom10to1]1.noorgigc - [wnom10to1]2.noorgigc = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.52
{txt}{col 10}Prob > chi2 =  {res}  0.4713
{txt}
{com}. 
. *** Appendix E, Table 6 ****
. esttab pronom2 pnom2 nnom2 nignom2 using example.tex, booktabs scalars("N_g Groups" "ll Log likelihood" "lrtest_chi2 LR test Chi$^2$") label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars transform(ln*: exp(2*@) 2*exp(2*@)) ///
>                  title(General Legislative Support (via all w-nominate scores) and Constituent Preferences  (Liberal Only) \label{c -(}tabplacebo{c )-}) ///
>                  indicate("Congress Year Dummies = *cnrptyy" ) ///
>                  addnotes("Coefficients are Log Odds. All independent variables are coded 0 to 1.") ///
>                  order(protest 1.poorhc 2.poorhc 1.nonwhitec 2.nonwhitec 1.noorgigc 2.noorgigc educ dem lengthserve votemargin blkrep hisprep womanrep) ///
>                  star(* 0.10 ** 0.05 *** 0.01) nobaselevels nodepvars
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
. *** Figure 3, All Roll Call Votes, Model I ***
. coefplot pronom2,  keep(protest)  vertical recast(bar)  /// 
>                 ciopts(lcolor(black) recast(rcap)) citop yline(0, lcolor(black) lpattern(dash)) ///
>                 nokey ylabel(-.04(.01).06) xlabel(1 "Protest")  graphregion(fcolor(white) lcolor(white)) ///
>                 title ("Model I", color(black)) xtitle (" ") bcolor(gray)
{res}{txt}
{com}. 
. *** Figure 3, All Roll Call Votes, Model II ***
. coefplot pnom2,  keep(1.poorhc 2.poorhc)  vertical recast(bar)  /// 
>                 ciopts(lcolor(black) recast(rcap)) citop yline(0, lcolor(black) lpattern(dash)) ///
>                 nokey ylabel(-.04(.01).06) xlabel(1 "Higher Income Participants" 2 "Low-Income Participants")  graphregion(fcolor(white) lcolor(white)) ///
>                 title ("Model II", color(black)) xtitle (" ") bcolor(gray)
{res}{txt}
{com}. 
. *** Figure 3, All Roll Call Votes, Model III ***
. coefplot nnom2,  keep(1.nonwhitec 2.nonwhitec)  vertical recast(bar)  /// 
>                 ciopts(lcolor(black) recast(rcap)) citop yline(0, lcolor(black) lpattern(dash)) ///
>                 nokey ylabel(-.04(.01).06) xlabel(1 "White Participants" 2 "Nonwhite Participants")  graphregion(fcolor(white) lcolor(white)) ///
>                 title ("Model III", color(black)) xtitle (" ") bcolor(gray)
{res}{txt}
{com}. 
. *** Figure 3, All Roll Call Votes, Model IV ***
. coefplot nignom2,  keep(1.noorgigc 2.noorgigc)  vertical recast(bar)  /// 
>                 ciopts(lcolor(black) recast(rcap)) citop yline(0, lcolor(black) lpattern(dash)) ///
>                 nokey ylabel(-.04(.01).06) xlabel(1 "Organized Interest Group" 2 "No Organized Interest Group")  graphregion(fcolor(white) lcolor(white)) ///
>                 title ("Model IV", color(black)) xtitle (" ") bcolor(gray)
{res}{txt}
{com}. 
. 
. *******************************************
. *******************************************
. ******** Alternative Explanations *********
. *******************************************
. *******************************************
. 
. 
. ***** Size *****
. ***** Size *****
. ***** Size *****
. 
. ttest osize if support!=., by(poorh)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Higher I {c |}{res}{col 12}  2,906{col 22} 2.679972{col 34} .0258163{col 46} 1.391686{col 58} 2.629352{col 70} 2.730593
{txt}Low-Inco {c |}{res}{col 12}     26{col 22} 2.538462{col 34} .0997037{col 46} .5083911{col 58} 2.333118{col 70} 2.743805
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,932{col 22} 2.678718{col 34} .0256032{col 46} 1.386358{col 58} 2.628516{col 70}  2.72892
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1415109{col 34} .2731349{col 58}-.3940449{col 70} .6770668
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Higher I{txt}) - mean({res}Low-Inco{txt})                        t = {res}  0.5181
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2930

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.6978         {txt}Pr(|T| > |t|) = {res}0.6044          {txt}Pr(T > t) = {res}0.3022
{txt}
{com}. eststo psize:  melogit supportall2 poorh##osize   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}{txt}note: 1.poorh#2.osize != 0 predicts success perfectly
      1.poorh#2.osize dropped and 12 obs not used

note: 1.poorh#1.osize identifies no observations in the sample
note: 1.poorh#3.osize omitted because of collinearity
note: 1.poorh#4.osize identifies no observations in the sample
note: 1.poorh#5.osize identifies no observations in the sample
note: 1.poorh#6.osize identifies no observations in the sample
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,212
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       212

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.4
{col 63}{txt}max{col 67}={res}{col 69}       108

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}    82.18
{txt}Log likelihood = {res}-1475.3251{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 36}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                        supportall2{col 37}{c |}      Coef.{col 49}   Std. Err.{col 61}      z{col 69}   P>|z|{col 77}     [95% Con{col 90}f. Interval]
{hline 36}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 35} {c |}
{space 30}poorh {c |}
{space 11}Low-Income Participants  {c |}{col 37}{res}{space 2} .8449231{col 49}{space 2} .6158994{col 60}{space 1}    1.37{col 69}{space 3}0.170{col 77}{space 4}-.3622176{col 90}{space 3} 2.052064
{txt}{space 35} {c |}
{space 30}osize {c |}
{space 29}Group  {c |}{col 37}{res}{space 2} .3239883{col 49}{space 2} .1283319{col 60}{space 1}    2.52{col 69}{space 3}0.012{col 77}{space 4} .0724624{col 90}{space 3} .5755143
{txt}{space 29}Large  {c |}{col 37}{res}{space 2} .4382637{col 49}{space 2} .1530693{col 60}{space 1}    2.86{col 69}{space 3}0.004{col 77}{space 4} .1382533{col 90}{space 3}  .738274
{txt}{space 26}Hundreds  {c |}{col 37}{res}{space 2} .3271964{col 49}{space 2} .1287702{col 60}{space 1}    2.54{col 69}{space 3}0.011{col 77}{space 4} .0748115{col 90}{space 3} .5795812
{txt}{space 25}Thousands  {c |}{col 37}{res}{space 2}-.2539446{col 49}{space 2} .1903384{col 60}{space 1}   -1.33{col 69}{space 3}0.182{col 77}{space 4}-.6270011{col 90}{space 3} .1191119
{txt}{space 17}Tens of Thousands  {c |}{col 37}{res}{space 2}-1.924362{col 49}{space 2}  .406867{col 60}{space 1}   -4.73{col 69}{space 3}0.000{col 77}{space 4}-2.721807{col 90}{space 3}-1.126917
{txt}{space 35} {c |}
{space 24}poorh#osize {c |}
{space 5}Low-Income Participants#Small  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 5}Low-Income Participants#Group  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 5}Low-Income Participants#Large  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (omitted)
{space 2}Low-Income Participants#Hundreds  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 1}Low-Income Participants#Thousands  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 11}Low-Income Participants #{c |}
{space 17}Tens of Thousands  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 35} {c |}
{space 31}educ {c |}{col 37}{res}{space 2} -.427128{col 49}{space 2} .3324302{col 60}{space 1}   -1.28{col 69}{space 3}0.199{col 77}{space 4}-1.078679{col 90}{space 3} .2244233
{txt}{space 32}dem {c |}{col 37}{res}{space 2} .4698429{col 49}{space 2}  .156053{col 60}{space 1}    3.01{col 69}{space 3}0.003{col 77}{space 4} .1639846{col 90}{space 3} .7757012
{txt}{space 24}lengthserve {c |}{col 37}{res}{space 2}-.0516326{col 49}{space 2} .4323406{col 60}{space 1}   -0.12{col 69}{space 3}0.905{col 77}{space 4}-.8990047{col 90}{space 3} .7957394
{txt}{space 25}votemargin {c |}{col 37}{res}{space 2} .5280658{col 49}{space 2}  .499821{col 60}{space 1}    1.06{col 69}{space 3}0.291{col 77}{space 4}-.4515655{col 90}{space 3} 1.507697
{txt}{space 29}blkrep {c |}{col 37}{res}{space 2}-.5862146{col 49}{space 2} .2154317{col 60}{space 1}   -2.72{col 69}{space 3}0.007{col 77}{space 4}-1.008453{col 90}{space 3}-.1639764
{txt}{space 28}hisprep {c |}{col 37}{res}{space 2}-.4731375{col 49}{space 2} .2581769{col 60}{space 1}   -1.83{col 69}{space 3}0.067{col 77}{space 4}-.9791549{col 90}{space 3}   .03288
{txt}{space 27}womanrep {c |}{col 37}{res}{space 2} .2676796{col 49}{space 2} .1881944{col 60}{space 1}    1.42{col 69}{space 3}0.155{col 77}{space 4}-.1011745{col 90}{space 3} .6365338
{txt}{space 35} {c |}
{space 28}cnrptyy {c |}
{space 31}103  {c |}{col 37}{res}{space 2} .4893235{col 49}{space 2} .1196124{col 60}{space 1}    4.09{col 69}{space 3}0.000{col 77}{space 4} .2548874{col 90}{space 3} .7237595
{txt}{space 31}104  {c |}{col 37}{res}{space 2} .5501934{col 49}{space 2}  .137575{col 60}{space 1}    4.00{col 69}{space 3}0.000{col 77}{space 4} .2805514{col 90}{space 3} .8198354
{txt}{space 35} {c |}
{space 30}_cons {c |}{col 37}{res}{space 2} -.700639{col 49}{space 2} .3767623{col 60}{space 1}   -1.86{col 69}{space 3}0.063{col 77}{space 4} -1.43908{col 90}{space 3} .0378016
{txt}{hline 36}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                            {col 37}{c |}
                          var(_cons){c |}{col 37}{res}{space 2} .2860549{col 49}{space 2} .1315394{col 77}{space 4} .1161537{col 90}{space 3}  .704475
{txt}{hline 36}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 14.36{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0001
{txt}
{com}. margins, dydx(poorh) at(osize=(1(1)6)) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,212
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.poorh}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9936709 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0063291 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3991546 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7906872 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1662154 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7296203 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2396022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1107595 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1808318 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3856239 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 4}.380651 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2337251 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9936709 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0063291 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3991546 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7906872 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1662154 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7296203 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2396022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1107595 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1808318 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3856239 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 4}.380651 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2337251 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9936709 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0063291 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3991546 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7906872 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1662154 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7296203 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2396022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1107595 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1808318 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3856239 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 4}.380651 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2337251 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9936709 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0063291 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3991546 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7906872 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1662154 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7296203 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2396022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1107595 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1808318 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3856239 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 4}.380651 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2337251 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9936709 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0063291 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3991546 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7906872 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1662154 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7296203 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2396022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1107595 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1808318 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3856239 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 4}.380651 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2337251 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9936709 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0063291 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3991546 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7906872 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1662154 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7296203 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2396022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1107595 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1808318 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3856239 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 4}.380651 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2337251 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.poorh      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}2  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}3  {c |}{col 14}{res}{space 2} .1661433{col 26}{space 2} .1030521{col 37}{space 1}    1.61{col 46}{space 3}0.107{col 54}{space 4} -.035835{col 67}{space 3} .3681216
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}5  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}6  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 4A ***
. marginsplot, x(osize) xlabel(1 "0-9" 2 "10-49" 3 "50-99" 4"100-999" 5 "1,000-9,999" 6 "10,000+") graphregion(fcolor(white)) legend(off)  ///
> plotopts(mcolor(black) msize(small) lcolor(black) ) ///
> ciopts(lcolor(black)) xsize(7) ylabel(-.4(.2).6) ///
> title("(A) Resources (Low-Income vs Higher Income)", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Number of Persons at Event") yline(0, lcolor(g16))

{text}{p 2 6 2}Variables that uniquely identify margins: osize{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g16 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. ttest osize if support!=., by(nonwhite)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
White Pa {c |}{res}{col 12}  2,399{col 22} 2.720717{col 34} .0283292{col 46} 1.387553{col 58} 2.665165{col 70} 2.776269
{txt}Nonwhite {c |}{res}{col 12}    533{col 22} 2.489681{col 34} .0591831{col 46} 1.366347{col 58}  2.37342{col 70} 2.605942
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,932{col 22} 2.678718{col 34} .0256032{col 46} 1.386358{col 58} 2.628516{col 70}  2.72892
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .2310359{col 34} .0662603{col 58} .1011143{col 70} .3609575
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}White Pa{txt}) - mean({res}Nonwhite{txt})                        t = {res}  3.4868
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2930

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9998         {txt}Pr(|T| > |t|) = {res}0.0005          {txt}Pr(T > t) = {res}0.0002
{txt}
{com}. eststo nwsize:  melogit supportall2 nonwhite##osize   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}{txt}note: 1.nonwhite#6.osize != 0 predicts failure perfectly
      1.nonwhite#6.osize dropped and 1 obs not used

{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,223
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       212

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.5
{col 63}{txt}max{col 67}={res}{col 69}       109

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}19{txt}){col 67}={res}{col 70}    95.30
{txt}Log likelihood = {res}-1474.2541{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                      supportall2{col 35}{c |}      Coef.{col 47}   Std. Err.{col 59}      z{col 67}   P>|z|{col 75}     [95% Con{col 88}f. Interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 33} {c |}
{space 25}nonwhite {c |}
{space 11}Nonwhite Participants  {c |}{col 35}{res}{space 2} .1498425{col 47}{space 2} .2072659{col 58}{space 1}    0.72{col 67}{space 3}0.470{col 75}{space 4}-.2563912{col 88}{space 3} .5560762
{txt}{space 33} {c |}
{space 28}osize {c |}
{space 27}Group  {c |}{col 35}{res}{space 2} .3491761{col 47}{space 2} .1410915{col 58}{space 1}    2.47{col 67}{space 3}0.013{col 75}{space 4} .0726418{col 88}{space 3} .6257105
{txt}{space 27}Large  {c |}{col 35}{res}{space 2} .4225394{col 47}{space 2} .1769577{col 58}{space 1}    2.39{col 67}{space 3}0.017{col 75}{space 4} .0757086{col 88}{space 3} .7693701
{txt}{space 24}Hundreds  {c |}{col 35}{res}{space 2} .1569333{col 47}{space 2} .1451957{col 58}{space 1}    1.08{col 67}{space 3}0.280{col 75}{space 4}-.1276451{col 88}{space 3} .4415116
{txt}{space 23}Thousands  {c |}{col 35}{res}{space 2}-.3889567{col 47}{space 2} .2211214{col 58}{space 1}   -1.76{col 67}{space 3}0.079{col 75}{space 4}-.8223467{col 88}{space 3} .0444332
{txt}{space 15}Tens of Thousands  {c |}{col 35}{res}{space 2}-1.892396{col 47}{space 2} .4098808{col 58}{space 1}   -4.62{col 67}{space 3}0.000{col 75}{space 4}-2.695747{col 88}{space 3}-1.089044
{txt}{space 33} {c |}
{space 19}nonwhite#osize {c |}
{space 5}Nonwhite Participants#Group  {c |}{col 35}{res}{space 2}-.0100295{col 47}{space 2} .3288075{col 58}{space 1}   -0.03{col 67}{space 3}0.976{col 75}{space 4}-.6544804{col 88}{space 3} .6344214
{txt}{space 5}Nonwhite Participants#Large  {c |}{col 35}{res}{space 2} .0754701{col 47}{space 2} .3436629{col 58}{space 1}    0.22{col 67}{space 3}0.826{col 75}{space 4}-.5980967{col 88}{space 3} .7490369
{txt}{space 2}Nonwhite Participants#Hundreds  {c |}{col 35}{res}{space 2} .7479893{col 47}{space 2} .3235034{col 58}{space 1}    2.31{col 67}{space 3}0.021{col 75}{space 4} .1139343{col 88}{space 3} 1.382044
{txt}{space 1}Nonwhite Participants#Thousands  {c |}{col 35}{res}{space 2} .3139498{col 47}{space 2} .4267006{col 58}{space 1}    0.74{col 67}{space 3}0.462{col 75}{space 4}-.5223681{col 88}{space 3} 1.150268
{txt}{space 11}Nonwhite Participants #{c |}
{space 15}Tens of Thousands  {c |}{col 35}{res}{space 2}        0{col 47}{txt}  (empty)
{space 33} {c |}
{space 29}educ {c |}{col 35}{res}{space 2}-.4155065{col 47}{space 2} .3340212{col 58}{space 1}   -1.24{col 67}{space 3}0.214{col 75}{space 4}-1.070176{col 88}{space 3}  .239163
{txt}{space 30}dem {c |}{col 35}{res}{space 2} .4817576{col 47}{space 2} .1568918{col 58}{space 1}    3.07{col 67}{space 3}0.002{col 75}{space 4} .1742554{col 88}{space 3} .7892597
{txt}{space 22}lengthserve {c |}{col 35}{res}{space 2}-.0897365{col 47}{space 2} .4335232{col 58}{space 1}   -0.21{col 67}{space 3}0.836{col 75}{space 4}-.9394263{col 88}{space 3} .7599533
{txt}{space 23}votemargin {c |}{col 35}{res}{space 2} .6661612{col 47}{space 2} .5028602{col 58}{space 1}    1.32{col 67}{space 3}0.185{col 75}{space 4}-.3194266{col 88}{space 3} 1.651749
{txt}{space 27}blkrep {c |}{col 35}{res}{space 2}-.6352834{col 47}{space 2}  .217619{col 58}{space 1}   -2.92{col 67}{space 3}0.004{col 75}{space 4}-1.061809{col 88}{space 3} -.208758
{txt}{space 26}hisprep {c |}{col 35}{res}{space 2}-.5287796{col 47}{space 2}  .260589{col 58}{space 1}   -2.03{col 67}{space 3}0.042{col 75}{space 4}-1.039525{col 88}{space 3}-.0180345
{txt}{space 25}womanrep {c |}{col 35}{res}{space 2} .2855065{col 47}{space 2} .1900016{col 58}{space 1}    1.50{col 67}{space 3}0.133{col 75}{space 4}-.0868898{col 88}{space 3} .6579027
{txt}{space 33} {c |}
{space 26}cnrptyy {c |}
{space 29}103  {c |}{col 35}{res}{space 2} .4608103{col 47}{space 2} .1202597{col 58}{space 1}    3.83{col 67}{space 3}0.000{col 75}{space 4} .2251057{col 88}{space 3}  .696515
{txt}{space 29}104  {c |}{col 35}{res}{space 2}  .406598{col 47}{space 2} .1427597{col 58}{space 1}    2.85{col 67}{space 3}0.004{col 75}{space 4} .1267942{col 88}{space 3} .6864018
{txt}{space 33} {c |}
{space 28}_cons {c |}{col 35}{res}{space 2}-.7811062{col 47}{space 2} .3793615{col 58}{space 1}   -2.06{col 67}{space 3}0.039{col 75}{space 4}-1.524641{col 88}{space 3}-.0375713
{txt}{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                          {col 35}{c |}
                        var(_cons){c |}{col 35}{res}{space 2} .2963696{col 47}{space 2} .1381795{col 75}{space 4} .1188418{col 88}{space 3} .7390917
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 13.70{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0001
{txt}
{com}. margins, dydx(nonwhite) at(osize=(1(1)6)) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,223
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.nonwhite}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7926226 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2073774 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3994068 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912731 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1658661 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7295367 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2393162 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1111111 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1812865 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2325686 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7926226 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2073774 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3994068 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912731 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1658661 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7295367 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2393162 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1111111 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1812865 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2325686 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7926226 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2073774 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3994068 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912731 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1658661 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7295367 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2393162 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1111111 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1812865 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2325686 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7926226 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2073774 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3994068 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912731 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1658661 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7295367 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2393162 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1111111 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1812865 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2325686 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7926226 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2073774 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3994068 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912731 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1658661 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7295367 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2393162 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1111111 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1812865 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2325686 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7926226 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2073774 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3994068 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912731 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1658661 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7295367 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2393162 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1111111 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1812865 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3837157 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2325686 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.nonwhite   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0349524{col 26}{space 2} .0482779{col 37}{space 1}    0.72{col 46}{space 3}0.469{col 54}{space 4}-.0596705{col 67}{space 3} .1295753
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0314166{col 26}{space 2} .0572489{col 37}{space 1}    0.55{col 46}{space 3}0.583{col 54}{space 4}-.0807892{col 67}{space 3} .1436224
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0494337{col 26}{space 2}  .060467{col 37}{space 1}    0.82{col 46}{space 3}0.414{col 54}{space 4}-.0690795{col 67}{space 3} .1679468
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1917222{col 26}{space 2} .0496368{col 37}{space 1}    3.86{col 46}{space 3}0.000{col 54}{space 4} .0944357{col 67}{space 3} .2890086
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1075891{col 26}{space 2} .0867029{col 37}{space 1}    1.24{col 46}{space 3}0.215{col 54}{space 4}-.0623454{col 67}{space 3} .2775237
{txt}{space 10}6  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 4B ***
. marginsplot, x(osize) xlabel(1 "0-9" 2 "10-49" 3 "50-99" 4"100-999" 5 "1,000-9,999" 6 "10,000+") graphregion(fcolor(white)) legend(off)  ///
> plotopts(mcolor(black) msize(small) lcolor(black) ) ///
> ciopts(lcolor(black)) xsize(7) ylabel(-.4(.2).6) ///
> title("(B) Resources (Nonwhite vs White)", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Number of Persons at Event") yline(0, lcolor(g16))

{text}{p 2 6 2}Variables that uniquely identify margins: osize{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g16 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. ttest osize if support!=., by(noorgig)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Organize {c |}{res}{col 12}  1,188{col 22} 2.820707{col 34}  .038524{col 46} 1.327821{col 58} 2.745124{col 70}  2.89629
{txt}No Organ {c |}{res}{col 12}  1,728{col 22} 2.568866{col 34} .0340788{col 46} 1.416628{col 58} 2.502026{col 70} 2.635706
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,916{col 22} 2.671468{col 34} .0256748{col 46} 1.386441{col 58} 2.621125{col 70}  2.72181
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .2518413{col 34} .0520538{col 58} .1497754{col 70} .3539072
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Organize{txt}) - mean({res}No Organ{txt})                        t = {res}  4.8381
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2914

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. eststo igsize: melogit supportall2 noorgig##osize   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,208
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       211

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.5
{col 63}{txt}max{col 67}={res}{col 69}       108

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}20{txt}){col 67}={res}{col 70}   102.76
{txt}Log likelihood = {res}-1447.8813{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 38}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                          supportall2{col 39}{c |}      Coef.{col 51}   Std. Err.{col 63}      z{col 71}   P>|z|{col 79}     [95% Con{col 92}f. Interval]
{hline 38}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 37} {c |}
{space 30}noorgig {c |}
{space 9}No Organized Interest Group  {c |}{col 39}{res}{space 2} .0388086{col 51}{space 2} .1944767{col 62}{space 1}    0.20{col 71}{space 3}0.842{col 79}{space 4}-.3423586{col 92}{space 3} .4199759
{txt}{space 37} {c |}
{space 32}osize {c |}
{space 31}Group  {c |}{col 39}{res}{space 2}  .279035{col 51}{space 2} .2160787{col 62}{space 1}    1.29{col 71}{space 3}0.197{col 79}{space 4}-.1444716{col 92}{space 3} .7025415
{txt}{space 31}Large  {c |}{col 39}{res}{space 2} .4286612{col 51}{space 2} .2321662{col 62}{space 1}    1.85{col 71}{space 3}0.065{col 79}{space 4}-.0263762{col 92}{space 3} .8836985
{txt}{space 28}Hundreds  {c |}{col 39}{res}{space 2} .2326734{col 51}{space 2} .2151464{col 62}{space 1}    1.08{col 71}{space 3}0.279{col 79}{space 4}-.1890057{col 92}{space 3} .6543526
{txt}{space 27}Thousands  {c |}{col 39}{res}{space 2}-2.500804{col 51}{space 2} .5510961{col 62}{space 1}   -4.54{col 71}{space 3}0.000{col 79}{space 4}-3.580933{col 92}{space 3}-1.420676
{txt}{space 19}Tens of Thousands  {c |}{col 39}{res}{space 2}-1.518046{col 51}{space 2} .6044929{col 62}{space 1}   -2.51{col 71}{space 3}0.012{col 79}{space 4} -2.70283{col 92}{space 3}-.3332615
{txt}{space 37} {c |}
{space 24}noorgig#osize {c |}
{space 3}No Organized Interest Group#Group  {c |}{col 39}{res}{space 2}   .13768{col 51}{space 2}  .264802{col 62}{space 1}    0.52{col 71}{space 3}0.603{col 79}{space 4}-.3813225{col 92}{space 3} .6566825
{txt}{space 3}No Organized Interest Group#Large  {c |}{col 39}{res}{space 2} .1453863{col 51}{space 2} .3151671{col 62}{space 1}    0.46{col 71}{space 3}0.645{col 79}{space 4}-.4723298{col 92}{space 3} .7631025
{txt}No Organized Interest Group#Hundreds  {c |}{col 39}{res}{space 2} .0933764{col 51}{space 2} .2673405{col 62}{space 1}    0.35{col 71}{space 3}0.727{col 79}{space 4}-.4306015{col 92}{space 3} .6173542
{txt}{space 9}No Organized Interest Group #{c |}
{space 27}Thousands  {c |}{col 39}{res}{space 2} 2.973731{col 51}{space 2}  .594494{col 62}{space 1}    5.00{col 71}{space 3}0.000{col 79}{space 4} 1.808545{col 92}{space 3} 4.138918
{txt}{space 9}No Organized Interest Group #{c |}
{space 19}Tens of Thousands  {c |}{col 39}{res}{space 2}-.7226602{col 51}{space 2} .8158853{col 62}{space 1}   -0.89{col 71}{space 3}0.376{col 79}{space 4}-2.321766{col 92}{space 3} .8764455
{txt}{space 37} {c |}
{space 33}educ {c |}{col 39}{res}{space 2}-.4456522{col 51}{space 2}  .335531{col 62}{space 1}   -1.33{col 71}{space 3}0.184{col 79}{space 4}-1.103281{col 92}{space 3} .2119765
{txt}{space 34}dem {c |}{col 39}{res}{space 2} .4972256{col 51}{space 2} .1572847{col 62}{space 1}    3.16{col 71}{space 3}0.002{col 79}{space 4} .1889534{col 92}{space 3} .8054979
{txt}{space 26}lengthserve {c |}{col 39}{res}{space 2}-.1713025{col 51}{space 2} .4376996{col 62}{space 1}   -0.39{col 71}{space 3}0.696{col 79}{space 4}-1.029178{col 92}{space 3} .6865729
{txt}{space 27}votemargin {c |}{col 39}{res}{space 2} .6565898{col 51}{space 2}   .50176{col 62}{space 1}    1.31{col 71}{space 3}0.191{col 79}{space 4}-.3268416{col 92}{space 3} 1.640021
{txt}{space 31}blkrep {c |}{col 39}{res}{space 2}-.6208246{col 51}{space 2}  .217012{col 62}{space 1}   -2.86{col 71}{space 3}0.004{col 79}{space 4} -1.04616{col 92}{space 3}-.1954889
{txt}{space 30}hisprep {c |}{col 39}{res}{space 2}-.5416034{col 51}{space 2} .2599358{col 62}{space 1}   -2.08{col 71}{space 3}0.037{col 79}{space 4}-1.051068{col 92}{space 3}-.0321386
{txt}{space 29}womanrep {c |}{col 39}{res}{space 2} .2581189{col 51}{space 2} .1878806{col 62}{space 1}    1.37{col 71}{space 3}0.169{col 79}{space 4}-.1101202{col 92}{space 3}  .626358
{txt}{space 37} {c |}
{space 30}cnrptyy {c |}
{space 33}103  {c |}{col 39}{res}{space 2} .4989835{col 51}{space 2} .1222186{col 62}{space 1}    4.08{col 71}{space 3}0.000{col 79}{space 4} .2594393{col 92}{space 3} .7385276
{txt}{space 33}104  {c |}{col 39}{res}{space 2} .5466457{col 51}{space 2} .1412436{col 62}{space 1}    3.87{col 71}{space 3}0.000{col 79}{space 4} .2698134{col 92}{space 3} .8234781
{txt}{space 37} {c |}
{space 32}_cons {c |}{col 39}{res}{space 2}-.8280026{col 51}{space 2} .4093692{col 62}{space 1}   -2.02{col 71}{space 3}0.043{col 79}{space 4}-1.630351{col 92}{space 3}-.0256538
{txt}{hline 38}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                              {col 39}{c |}
                            var(_cons){c |}{col 39}{res}{space 2} .2732294{col 51}{space 2} .1329465{col 79}{space 4}  .105282{col 92}{space 3} .7090887
{txt}{hline 38}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 12.70{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0002
{txt}
{com}. margins, dydx(noorgig) at(osize=(1(1)6)) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,208
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.noorgig}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3913043 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6086957 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3990571 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912138 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294656 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2391304 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1109601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1811594 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3858696 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3799819 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2341486 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3913043 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6086957 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3990571 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912138 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294656 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2391304 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1109601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1811594 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3858696 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3799819 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2341486 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3913043 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6086957 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3990571 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912138 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294656 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2391304 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1109601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1811594 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3858696 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3799819 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2341486 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3913043 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6086957 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3990571 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912138 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294656 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2391304 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1109601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1811594 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3858696 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3799819 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2341486 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3913043 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6086957 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3990571 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912138 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294656 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2391304 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1109601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1811594 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3858696 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3799819 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2341486 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3913043 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6086957 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:osize}{space 11}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3990571 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7912138 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294656 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2391304 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1109601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1811594 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3858696 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3799819 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2341486 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.noorgig    {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0091152{col 26}{space 2} .0456798{col 37}{space 1}    0.20{col 46}{space 3}0.842{col 54}{space 4}-.0804155{col 67}{space 3}  .098646
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0403221{col 26}{space 2} .0419789{col 37}{space 1}    0.96{col 46}{space 3}0.337{col 54}{space 4}-.0419549{col 67}{space 3} .1225992
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0409262{col 26}{space 2} .0548442{col 37}{space 1}    0.75{col 46}{space 3}0.456{col 54}{space 4}-.0665665{col 67}{space 3} .1484189
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0304897{col 26}{space 2} .0427712{col 37}{space 1}    0.71{col 46}{space 3}0.476{col 54}{space 4}-.0533403{col 67}{space 3} .1143196
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .5331748{col 26}{space 2} .0609541{col 37}{space 1}    8.75{col 46}{space 3}0.000{col 54}{space 4}  .413707{col 67}{space 3} .6526426
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0823531{col 26}{space 2} .0999406{col 37}{space 1}   -0.82{col 46}{space 3}0.410{col 54}{space 4} -.278233{col 67}{space 3} .1135269
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 4C ***
. marginsplot, x(osize) xlabel(1 "0-9" 2 "10-49" 3 "50-99" 4"100-999" 5 "1,000-9,999" 6 "10,000+") graphregion(fcolor(white)) legend(off)  ///
> plotopts(mcolor(black) msize(small) lcolor(black) ) ///
> ciopts(lcolor(black)) xsize(7) ylabel(-.4(.2).6) ///
> title("(C) Organizational Resources", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Number of Persons at Event") yline(0, lcolor(g16))

{text}{p 2 6 2}Variables that uniquely identify margins: osize{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g16 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. *** Appendix E, Table 7 ****
. esttab psize nwsize igsize using example.tex, booktabs scalars("ll Log likelihood") label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars ///
>                  title(Resources, Salience (Size) and Legislative Support \label{c -(}tabfigsize{c )-}) ///
>                  mtitles("Model A" "Model B" "Model C" "Model D") ///
>                  indicate("Congress Year Dummies = *cnrptyy" ) ///
>                  drop(0.poorh 0.nonwhite 0.noorgig 0.poorh*osize 0.nonwhite*osize 0.noorgig*osize 1.poorh*1.osize 1.nonwhite*1.osize 1.noorgig*1.osize 1.osize educ  dem  lengthserve  votemargin blkrep hisprep womanrep) ///
>                  order(1.poorh 1.nonwhite 1.noorgig 2.osize 3.osize 4.osize 5.osize 6.osize 1.poorh*osize 1.nonwhite*osize 1.noorgig*osize) ///
>                  addnotes("Coefficients are Log Odds. The dependent variable, legislative support, is binary taking on a value of 0 or 1. " "All independent variables are coded 0 to 1." "Estimates informing Figure 2.")
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
.                  
. ***** Protest Frequency *****
. ***** Protest Frequency *****
. ***** Protest Frequency *****
. 
. by statecd cnrptyy rccount, sort: egen numprotest = count(protest) if protest==1 
{txt}(31055 missing values generated)

{com}. replace numprotest=0 if protestincd==0
{txt}(29,534 real changes made)

{com}. 
. by statecd cnrptyy rccount, sort: egen numprotestpoor = count(protest) if protest==1 & poorh==1
{txt}(35053 missing values generated)

{com}. by statecd cnrptyy rccount, sort: egen numprotestnp = count(protest) if protest==1 & poorh==0
{txt}(31097 missing values generated)

{com}. 
. gen numprotestp = .
{txt}(35,095 missing values generated)

{com}. replace numprotestp=numprotestpoor if poorh==1
{txt}(42 real changes made)

{com}. replace numprotestp=numprotestnp if poorh==0
{txt}(3,998 real changes made)

{com}. replace numprotestp=0 if protestincd==0
{txt}(29,534 real changes made)

{com}. 
. ttest numprotestp if support!=., by(poorh) 

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Higher I {c |}{res}{col 12}  2,986{col 22}   6.1929{col 34} .1129087{col 46} 6.169816{col 58} 5.971513{col 70} 6.414287
{txt}Low-Inco {c |}{res}{col 12}     26{col 22}        1{col 34}        0{col 46}        0{col 58}        1{col 70}        1
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  3,012{col 22} 6.148074{col 34} .1122757{col 46}  6.16188{col 58}  5.92793{col 70} 6.368219
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}   5.1929{col 34}   1.2102{col 58} 2.819998{col 70} 7.565802
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Higher I{txt}) - mean({res}Low-Inco{txt})                        t = {res}  4.2909
{txt}Ho: diff = 0                                     degrees of freedom = {res}    3010

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. label define freq 1 "1 Protest" 2 "2 Protests" 3 "3 Protests" 4 "4 Protests" 5 "5 Protests" 6 "6 Protests" 7 "7 Protests" 8 "8 Protests" 9 "9 Protests" 10 "10 Protests" 12 "12 Protests" 14 "14 Protests" 15 "15 Protests" 16 "16 Protests" 17 "17 Protests" 20 "20 Protests", modify
{txt}
{com}. label values numprotestp freq
{txt}
{com}. 
. *** Figure 5A ***
. eststo pfreq:  melogit supportall2 poorh##numprotestp   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}{txt}note: 0.numprotestp identifies no observations in the sample
note: 23.numprotestp omitted because of collinearity
note: 27.numprotestp identifies no observations in the sample
note: 0.poorh#0.numprotestp identifies no observations in the sample
note: 0.poorh#27.numprotestp identifies no observations in the sample
note: 1.poorh#0.numprotestp identifies no observations in the sample
note: 1.poorh#1.numprotestp omitted because of collinearity
note: 1.poorh#2.numprotestp identifies no observations in the sample
note: 1.poorh#3.numprotestp identifies no observations in the sample
note: 1.poorh#4.numprotestp identifies no observations in the sample
note: 1.poorh#5.numprotestp identifies no observations in the sample
note: 1.poorh#6.numprotestp identifies no observations in the sample
note: 1.poorh#7.numprotestp identifies no observations in the sample
note: 1.poorh#8.numprotestp identifies no observations in the sample
note: 1.poorh#10.numprotestp identifies no observations in the sample
note: 1.poorh#15.numprotestp identifies no observations in the sample
note: 1.poorh#16.numprotestp identifies no observations in the sample
note: 1.poorh#17.numprotestp identifies no observations in the sample
note: 1.poorh#20.numprotestp identifies no observations in the sample
note: 1.poorh#23.numprotestp identifies no observations in the sample
note: 1.poorh#27.numprotestp identifies no observations in the sample
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,301
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       213

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.8
{col 63}{txt}max{col 67}={res}{col 69}       112

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}23{txt}){col 67}={res}{col 70}   320.84
{txt}Log likelihood = {res}-1382.5576{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 38}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                          supportall2{col 39}{c |}      Coef.{col 51}   Std. Err.{col 63}      z{col 71}   P>|z|{col 79}     [95% Con{col 92}f. Interval]
{hline 38}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 37} {c |}
{space 32}poorh {c |}
{space 13}Low-Income Participants  {c |}{col 39}{res}{space 2}  1.06882{col 51}{space 2} .5695495{col 62}{space 1}    1.88{col 71}{space 3}0.061{col 79}{space 4} -.047476{col 92}{space 3} 2.185117
{txt}{space 37} {c |}
{space 26}numprotestp {c |}
{space 35}0  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 27}1 Protest  {c |}{col 39}{res}{space 2}-1.041835{col 51}{space 2} 1.370352{col 62}{space 1}   -0.76{col 71}{space 3}0.447{col 79}{space 4}-3.727676{col 92}{space 3} 1.644006
{txt}{space 26}2 Protests  {c |}{col 39}{res}{space 2}-.9386137{col 51}{space 2} 1.373146{col 62}{space 1}   -0.68{col 71}{space 3}0.494{col 79}{space 4}-3.629931{col 92}{space 3} 1.752703
{txt}{space 26}3 Protests  {c |}{col 39}{res}{space 2} .0038475{col 51}{space 2}  1.37798{col 62}{space 1}    0.00{col 71}{space 3}0.998{col 79}{space 4}-2.696944{col 92}{space 3} 2.704639
{txt}{space 26}4 Protests  {c |}{col 39}{res}{space 2}-1.031216{col 51}{space 2} 1.369895{col 62}{space 1}   -0.75{col 71}{space 3}0.452{col 79}{space 4}-3.716161{col 92}{space 3} 1.653729
{txt}{space 26}5 Protests  {c |}{col 39}{res}{space 2}-.0430722{col 51}{space 2} 1.388123{col 62}{space 1}   -0.03{col 71}{space 3}0.975{col 79}{space 4}-2.763743{col 92}{space 3} 2.677598
{txt}{space 26}6 Protests  {c |}{col 39}{res}{space 2}-2.870037{col 51}{space 2} 1.390144{col 62}{space 1}   -2.06{col 71}{space 3}0.039{col 79}{space 4} -5.59467{col 92}{space 3}-.1454038
{txt}{space 26}7 Protests  {c |}{col 39}{res}{space 2}-.4750254{col 51}{space 2} 1.400099{col 62}{space 1}   -0.34{col 71}{space 3}0.734{col 79}{space 4}-3.219169{col 92}{space 3} 2.269118
{txt}{space 26}8 Protests  {c |}{col 39}{res}{space 2}-1.607224{col 51}{space 2} 1.397503{col 62}{space 1}   -1.15{col 71}{space 3}0.250{col 79}{space 4} -4.34628{col 92}{space 3} 1.131832
{txt}{space 25}10 Protests  {c |}{col 39}{res}{space 2}-2.732274{col 51}{space 2} 1.396571{col 62}{space 1}   -1.96{col 71}{space 3}0.050{col 79}{space 4}-5.469502{col 92}{space 3} .0049535
{txt}{space 25}15 Protests  {c |}{col 39}{res}{space 2}-.7468932{col 51}{space 2} 1.607268{col 62}{space 1}   -0.46{col 71}{space 3}0.642{col 79}{space 4}-3.897081{col 92}{space 3} 2.403294
{txt}{space 25}16 Protests  {c |}{col 39}{res}{space 2}-2.149379{col 51}{space 2} 1.576975{col 62}{space 1}   -1.36{col 71}{space 3}0.173{col 79}{space 4}-5.240193{col 92}{space 3} .9414341
{txt}{space 25}17 Protests  {c |}{col 39}{res}{space 2}-3.340471{col 51}{space 2} 1.385902{col 62}{space 1}   -2.41{col 71}{space 3}0.016{col 79}{space 4}-6.056788{col 92}{space 3}-.6241535
{txt}{space 25}20 Protests  {c |}{col 39}{res}{space 2}-2.584738{col 51}{space 2} 1.378094{col 62}{space 1}   -1.88{col 71}{space 3}0.061{col 79}{space 4}-5.285752{col 92}{space 3} .1162767
{txt}{space 34}23  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (omitted)
{space 34}27  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 37} {c |}
{space 20}poorh#numprotestp {c |}
{space 7}Higher Income Participants# 0  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 7}Higher Income Participants#27  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 10}Low-Income Participants# 0  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 3}Low-Income Participants#1 Protest  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (omitted)
{space 2}Low-Income Participants#2 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 2}Low-Income Participants#3 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 2}Low-Income Participants#4 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 2}Low-Income Participants#5 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 2}Low-Income Participants#6 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 2}Low-Income Participants#7 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 2}Low-Income Participants#8 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 1}Low-Income Participants#10 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 1}Low-Income Participants#15 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 1}Low-Income Participants#16 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 1}Low-Income Participants#17 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 1}Low-Income Participants#20 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 10}Low-Income Participants#23  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 10}Low-Income Participants#27  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 37} {c |}
{space 33}educ {c |}{col 39}{res}{space 2}-.5145313{col 51}{space 2} .3603153{col 62}{space 1}   -1.43{col 71}{space 3}0.153{col 79}{space 4}-1.220736{col 92}{space 3} .1916737
{txt}{space 34}dem {c |}{col 39}{res}{space 2} .4336373{col 51}{space 2} .1665487{col 62}{space 1}    2.60{col 71}{space 3}0.009{col 79}{space 4} .1072078{col 92}{space 3} .7600667
{txt}{space 26}lengthserve {c |}{col 39}{res}{space 2}-.3205583{col 51}{space 2}  .452775{col 62}{space 1}   -0.71{col 71}{space 3}0.479{col 79}{space 4}-1.207981{col 92}{space 3} .5668645
{txt}{space 27}votemargin {c |}{col 39}{res}{space 2} .9586103{col 51}{space 2}  .539459{col 62}{space 1}    1.78{col 71}{space 3}0.076{col 79}{space 4}  -.09871{col 92}{space 3} 2.015931
{txt}{space 31}blkrep {c |}{col 39}{res}{space 2}-.5810936{col 51}{space 2} .2305429{col 62}{space 1}   -2.52{col 71}{space 3}0.012{col 79}{space 4}-1.032949{col 92}{space 3}-.1292378
{txt}{space 30}hisprep {c |}{col 39}{res}{space 2}-.5368615{col 51}{space 2} .2796567{col 62}{space 1}   -1.92{col 71}{space 3}0.055{col 79}{space 4}-1.084979{col 92}{space 3} .0112556
{txt}{space 29}womanrep {c |}{col 39}{res}{space 2} .4480926{col 51}{space 2} .2057425{col 62}{space 1}    2.18{col 71}{space 3}0.029{col 79}{space 4} .0448446{col 92}{space 3} .8513405
{txt}{space 37} {c |}
{space 30}cnrptyy {c |}
{space 33}103  {c |}{col 39}{res}{space 2}  .853115{col 51}{space 2}  .158049{col 62}{space 1}    5.40{col 71}{space 3}0.000{col 79}{space 4} .5433446{col 92}{space 3} 1.162885
{txt}{space 33}104  {c |}{col 39}{res}{space 2}-.0390537{col 51}{space 2}  .201216{col 62}{space 1}   -0.19{col 71}{space 3}0.846{col 79}{space 4}-.4334298{col 92}{space 3} .3553223
{txt}{space 37} {c |}
{space 32}_cons {c |}{col 39}{res}{space 2} .3191863{col 51}{space 2} 1.422965{col 62}{space 1}    0.22{col 71}{space 3}0.823{col 79}{space 4}-2.469773{col 92}{space 3} 3.108146
{txt}{hline 38}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                              {col 39}{c |}
                            var(_cons){c |}{col 39}{res}{space 2} .4224748{col 51}{space 2} .1466268{col 79}{space 4} .2139821{col 92}{space 3} .8341117
{txt}{hline 38}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 30.26{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. margins, dydx(poorh) at(numprotestp=(1(1)8)) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,301
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.poorh}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9887006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0112994 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestp}{space 5}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9887006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0112994 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestp}{space 5}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9887006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0112994 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestp}{space 5}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9887006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0112994 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestp}{space 5}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9887006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0112994 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestp}{space 5}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9887006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0112994 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestp}{space 5}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9887006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0112994 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestp}{space 5}{txt:=} {space 10}7}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9887006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0112994 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestp}{space 5}{txt:=} {space 10}8}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.poorh      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2129035{col 26}{space 2} .0944416{col 37}{space 1}    2.25{col 46}{space 3}0.024{col 54}{space 4} .0278013{col 67}{space 3} .3980057
{txt}{space 10}2  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}3  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}4  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}5  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}6  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}7  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}8  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, x(numprotestp) graphregion(fcolor(white)) legend(off)  ///
> plotopts(mcolor(black) msize(small) lcolor(black) ) ///
> ciopts(lcolor(black)) xsize(7) ylabel(-.6(.2).6) ///
> title("(A) Resources (Low-Income vs Higher Income)", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Number of Protests on Issue in District") yline(0, lcolor(g16))

{text}{p 2 6 2}Variables that uniquely identify margins: numprotestp{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g16 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. 
. by statecd cnrptyy rccount, sort: egen numprotestnonw = count(protest) if protest==1 & nonwhite==1
{txt}(34277 missing values generated)

{com}. by statecd cnrptyy rccount, sort: egen numprotestwhite = count(protest) if protest==1 & nonwhite==0
{txt}(31873 missing values generated)

{com}. 
. gen numprotestnw = .
{txt}(35,095 missing values generated)

{com}. replace numprotestnw=numprotestnonw if nonwhite==1
{txt}(818 real changes made)

{com}. replace numprotestnw=numprotestwhite if nonwhite==0
{txt}(3,222 real changes made)

{com}. replace numprotestnw=0 if protestincd==0
{txt}(29,534 real changes made)

{com}. 
. label values numprotestnw freq
{txt}
{com}. 
. ttest numprotestnw if support!=., by(nonwhite)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
White Pa {c |}{res}{col 12}  2,471{col 22} 6.387293{col 34} .1347634{col 46} 6.698975{col 58} 6.123032{col 70} 6.651553
{txt}Nonwhite {c |}{res}{col 12}    541{col 22} 2.504621{col 34}  .078696{col 46} 1.830421{col 58} 2.350033{col 70} 2.659209
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  3,012{col 22} 5.689907{col 34} .1147144{col 46} 6.295723{col 58}  5.46498{col 70} 5.914834
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} 3.882672{col 34} .2903901{col 58} 3.313288{col 70} 4.452055
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}White Pa{txt}) - mean({res}Nonwhite{txt})                        t = {res} 13.3705
{txt}Ho: diff = 0                                     degrees of freedom = {res}    3010

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. eststo nwfreq: melogit supportall2 i.nonwhite##i.numprotestnw   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}{txt}note: 0.nonwhite#8.numprotestnw != 0 predicts success perfectly
      0.nonwhite#8.numprotestnw dropped and 4 obs not used

note: 8.numprotestnw != 0 predicts failure perfectly
      8.numprotestnw dropped and 4 obs not used

note: 0.numprotestnw identifies no observations in the sample
note: 9.numprotestnw identifies no observations in the sample
note: 12.numprotestnw identifies no observations in the sample
note: 14.numprotestnw identifies no observations in the sample
note: 23.numprotestnw omitted because of collinearity
note: 0.nonwhite#0.numprotestnw identifies no observations in the sample
note: 0.nonwhite#9.numprotestnw identifies no observations in the sample
note: 0.nonwhite#12.numprotestnw identifies no observations in the sample
note: 0.nonwhite#14.numprotestnw identifies no observations in the sample
note: 1.nonwhite#0.numprotestnw identifies no observations in the sample
note: 1.nonwhite#5.numprotestnw identifies no observations in the sample
note: 1.nonwhite#6.numprotestnw omitted because of collinearity
note: 1.nonwhite#7.numprotestnw identifies no observations in the sample
note: 1.nonwhite#8.numprotestnw omitted because of collinearity
note: 1.nonwhite#9.numprotestnw identifies no observations in the sample
note: 1.nonwhite#10.numprotestnw identifies no observations in the sample
note: 1.nonwhite#12.numprotestnw identifies no observations in the sample
note: 1.nonwhite#14.numprotestnw identifies no observations in the sample
note: 1.nonwhite#15.numprotestnw identifies no observations in the sample
note: 1.nonwhite#16.numprotestnw identifies no observations in the sample
note: 1.nonwhite#17.numprotestnw identifies no observations in the sample
note: 1.nonwhite#20.numprotestnw identifies no observations in the sample
note: 1.nonwhite#23.numprotestnw identifies no observations in the sample
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,293
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       213

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.8
{col 63}{txt}max{col 67}={res}{col 69}       112

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}26{txt}){col 67}={res}{col 70}   290.29
{txt}Log likelihood = {res}-1400.5136{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 36}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                        supportall2{col 37}{c |}      Coef.{col 49}   Std. Err.{col 61}      z{col 69}   P>|z|{col 77}     [95% Con{col 90}f. Interval]
{hline 36}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 35} {c |}
{space 27}nonwhite {c |}
{space 13}Nonwhite Participants  {c |}{col 37}{res}{space 2} 1.800076{col 49}{space 2} .4501402{col 60}{space 1}    4.00{col 69}{space 3}0.000{col 77}{space 4} .9178177{col 90}{space 3} 2.682335
{txt}{space 35} {c |}
{space 23}numprotestnw {c |}
{space 33}0  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 25}1 Protest  {c |}{col 37}{res}{space 2}-1.094198{col 49}{space 2} 1.335105{col 60}{space 1}   -0.82{col 69}{space 3}0.412{col 77}{space 4}-3.710956{col 90}{space 3} 1.522561
{txt}{space 24}2 Protests  {c |}{col 37}{res}{space 2}-.8470986{col 49}{space 2} 1.336699{col 60}{space 1}   -0.63{col 69}{space 3}0.526{col 77}{space 4} -3.46698{col 90}{space 3} 1.772783
{txt}{space 24}3 Protests  {c |}{col 37}{res}{space 2}-.9661702{col 49}{space 2} 1.340642{col 60}{space 1}   -0.72{col 69}{space 3}0.471{col 77}{space 4} -3.59378{col 90}{space 3}  1.66144
{txt}{space 24}4 Protests  {c |}{col 37}{res}{space 2}-.7046366{col 49}{space 2} 1.337364{col 60}{space 1}   -0.53{col 69}{space 3}0.598{col 77}{space 4}-3.325821{col 90}{space 3} 1.916548
{txt}{space 24}5 Protests  {c |}{col 37}{res}{space 2} .0313805{col 49}{space 2} 1.390243{col 60}{space 1}    0.02{col 69}{space 3}0.982{col 77}{space 4}-2.693446{col 90}{space 3} 2.756207
{txt}{space 24}6 Protests  {c |}{col 37}{res}{space 2}-3.154297{col 49}{space 2} 1.379841{col 60}{space 1}   -2.29{col 69}{space 3}0.022{col 77}{space 4}-5.858736{col 90}{space 3}-.4498587
{txt}{space 24}7 Protests  {c |}{col 37}{res}{space 2}-.3093966{col 49}{space 2} 1.365218{col 60}{space 1}   -0.23{col 69}{space 3}0.821{col 77}{space 4}-2.985175{col 90}{space 3} 2.366382
{txt}{space 24}8 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 24}9 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 23}10 Protests  {c |}{col 37}{res}{space 2}-2.658864{col 49}{space 2} 1.362106{col 60}{space 1}   -1.95{col 69}{space 3}0.051{col 77}{space 4}-5.328543{col 90}{space 3} .0108156
{txt}{space 23}12 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 23}14 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 23}15 Protests  {c |}{col 37}{res}{space 2}-.8171978{col 49}{space 2} 1.564995{col 60}{space 1}   -0.52{col 69}{space 3}0.602{col 77}{space 4}-3.884532{col 90}{space 3} 2.250136
{txt}{space 23}16 Protests  {c |}{col 37}{res}{space 2}-2.120572{col 49}{space 2} 1.533973{col 60}{space 1}   -1.38{col 69}{space 3}0.167{col 77}{space 4}-5.127104{col 90}{space 3} .8859603
{txt}{space 23}17 Protests  {c |}{col 37}{res}{space 2}-3.125534{col 49}{space 2}  1.34931{col 60}{space 1}   -2.32{col 69}{space 3}0.021{col 77}{space 4}-5.770133{col 90}{space 3}-.4809357
{txt}{space 23}20 Protests  {c |}{col 37}{res}{space 2}-2.504034{col 49}{space 2} 1.343165{col 60}{space 1}   -1.86{col 69}{space 3}0.062{col 77}{space 4}-5.136589{col 90}{space 3} .1285208
{txt}{space 32}23  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (omitted)
{space 35} {c |}
{space 14}nonwhite#numprotestnw {c |}
{space 13}White Participants# 0  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 5}White Participants#8 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 5}White Participants#9 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 4}White Participants#12 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 4}White Participants#14 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 10}Nonwhite Participants# 0  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 3}Nonwhite Participants#1 Protest  {c |}{col 37}{res}{space 2}-1.047184{col 49}{space 2}  .488174{col 60}{space 1}   -2.15{col 69}{space 3}0.032{col 77}{space 4}-2.003988{col 90}{space 3}-.0903809
{txt}{space 2}Nonwhite Participants#2 Protests  {c |}{col 37}{res}{space 2}-2.392033{col 49}{space 2} .5153283{col 60}{space 1}   -4.64{col 69}{space 3}0.000{col 77}{space 4}-3.402058{col 90}{space 3}-1.382008
{txt}{space 2}Nonwhite Participants#3 Protests  {c |}{col 37}{res}{space 2}-.7510183{col 49}{space 2} .5535551{col 60}{space 1}   -1.36{col 69}{space 3}0.175{col 77}{space 4}-1.835966{col 90}{space 3} .3339298
{txt}{space 2}Nonwhite Participants#4 Protests  {c |}{col 37}{res}{space 2} -3.20449{col 49}{space 2} .8504413{col 60}{space 1}   -3.77{col 69}{space 3}0.000{col 77}{space 4}-4.871325{col 90}{space 3}-1.537656
{txt}{space 2}Nonwhite Participants#5 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 2}Nonwhite Participants#6 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (omitted)
{space 2}Nonwhite Participants#7 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 2}Nonwhite Participants#8 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 2}Nonwhite Participants#9 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 1}Nonwhite Participants#10 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 1}Nonwhite Participants#12 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 1}Nonwhite Participants#14 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 1}Nonwhite Participants#15 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 1}Nonwhite Participants#16 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 1}Nonwhite Participants#17 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 1}Nonwhite Participants#20 Protests  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 10}Nonwhite Participants#23  {c |}{col 37}{res}{space 2}        0{col 49}{txt}  (empty)
{space 35} {c |}
{space 31}educ {c |}{col 37}{res}{space 2}-.5294195{col 49}{space 2} .3526887{col 60}{space 1}   -1.50{col 69}{space 3}0.133{col 77}{space 4}-1.220677{col 90}{space 3} .1618377
{txt}{space 32}dem {c |}{col 37}{res}{space 2} .4088021{col 49}{space 2} .1634344{col 60}{space 1}    2.50{col 69}{space 3}0.012{col 77}{space 4} .0884767{col 90}{space 3} .7291276
{txt}{space 24}lengthserve {c |}{col 37}{res}{space 2}-.4314017{col 49}{space 2} .4435659{col 60}{space 1}   -0.97{col 69}{space 3}0.331{col 77}{space 4}-1.300775{col 90}{space 3} .4379715
{txt}{space 25}votemargin {c |}{col 37}{res}{space 2} .9834474{col 49}{space 2} .5286888{col 60}{space 1}    1.86{col 69}{space 3}0.063{col 77}{space 4}-.0527636{col 90}{space 3} 2.019658
{txt}{space 29}blkrep {c |}{col 37}{res}{space 2}-.5824548{col 49}{space 2} .2261956{col 60}{space 1}   -2.58{col 69}{space 3}0.010{col 77}{space 4} -1.02579{col 90}{space 3}-.1391196
{txt}{space 28}hisprep {c |}{col 37}{res}{space 2}-.5820179{col 49}{space 2} .2746073{col 60}{space 1}   -2.12{col 69}{space 3}0.034{col 77}{space 4}-1.120238{col 90}{space 3}-.0437974
{txt}{space 27}womanrep {c |}{col 37}{res}{space 2} .3526426{col 49}{space 2} .1976553{col 60}{space 1}    1.78{col 69}{space 3}0.074{col 77}{space 4}-.0347548{col 90}{space 3} .7400399
{txt}{space 35} {c |}
{space 28}cnrptyy {c |}
{space 31}103  {c |}{col 37}{res}{space 2} .7347684{col 49}{space 2} .1466558{col 60}{space 1}    5.01{col 69}{space 3}0.000{col 77}{space 4} .4473284{col 90}{space 3} 1.022208
{txt}{space 31}104  {c |}{col 37}{res}{space 2} .0786826{col 49}{space 2}  .167539{col 60}{space 1}    0.47{col 69}{space 3}0.639{col 77}{space 4}-.2496878{col 90}{space 3} .4070531
{txt}{space 35} {c |}
{space 30}_cons {c |}{col 37}{res}{space 2} .3503752{col 49}{space 2} 1.386664{col 60}{space 1}    0.25{col 69}{space 3}0.801{col 77}{space 4}-2.367436{col 90}{space 3} 3.068186
{txt}{hline 36}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                            {col 37}{c |}
                          var(_cons){c |}{col 37}{res}{space 2} .3437993{col 49}{space 2} .1366437{col 77}{space 4} .1577586{col 90}{space 3} .7492327
{txt}{hline 36}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 21.19{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. margins, dydx(nonwhite) at(numprotestnw=(1/8)) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,293
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.nonwhite}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7967728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2032272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnw}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3998788 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7941561 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659484 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292412 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2385521 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1120802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1822939 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3820323 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3877017 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.230266 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7967728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2032272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnw}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3998788 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7941561 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659484 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292412 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2385521 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1120802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1822939 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3820323 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3877017 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.230266 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7967728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2032272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnw}{space 4}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3998788 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7941561 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659484 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292412 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2385521 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1120802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1822939 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3820323 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3877017 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.230266 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7967728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2032272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnw}{space 4}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3998788 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7941561 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659484 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292412 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2385521 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1120802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1822939 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3820323 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3877017 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.230266 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7967728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2032272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnw}{space 4}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3998788 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7941561 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659484 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292412 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2385521 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1120802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1822939 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3820323 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3877017 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.230266 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7967728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2032272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnw}{space 4}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3998788 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7941561 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659484 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292412 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2385521 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1120802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1822939 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3820323 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3877017 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.230266 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7967728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2032272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnw}{space 4}{txt:=} {space 10}7}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3998788 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7941561 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659484 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292412 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2385521 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1120802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1822939 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3820323 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3877017 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.230266 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7967728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2032272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnw}{space 4}{txt:=} {space 10}8}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3998788 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7941561 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1659484 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292412 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2385521 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1120802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1822939 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3820323 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3877017 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.230266 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.nonwhite   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1626221{col 26}{space 2} .0400875{col 37}{space 1}    4.06{col 46}{space 3}0.000{col 54}{space 4} .0840521{col 67}{space 3} .2411921
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1357973{col 26}{space 2}  .058485{col 37}{space 1}   -2.32{col 46}{space 3}0.020{col 54}{space 4}-.2504259{col 67}{space 3}-.0211688
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2093166{col 26}{space 2} .0607236{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4} .0903005{col 67}{space 3} .3283327
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.3137062{col 26}{space 2} .1505202{col 37}{space 1}   -2.08{col 46}{space 3}0.037{col 54}{space 4}-.6087204{col 67}{space 3}-.0186919
{txt}{space 10}5  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}6  {c |}{col 14}{res}{space 2} .3348741{col 26}{space 2} .0792846{col 37}{space 1}    4.22{col 46}{space 3}0.000{col 54}{space 4} .1794792{col 67}{space 3}  .490269
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{space 10}8  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 5B ***
. marginsplot, x(numprotestnw) xtitle("Number of Protests on Issue in District") graphregion(fcolor(white)) legend(off)  ///
> plotopts(mcolor(black) msize(small) lcolor(black) ) ///
> ciopts(lcolor(black)) xsize(7) ylabel(-.6(.2).6) ///
> title(" (B) Resources (Nonwhite vs White)", color(black))  ///
> ytitle("Difference in Effect of Resources") yline(0, lcolor(g16)) 

{text}{p 2 6 2}Variables that uniquely identify margins: numprotestnw{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g16 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. 
. by statecd cnrptyy rccount, sort: egen numprotestnoorg = count(protest) if protest==1 & noorgig==1
{txt}(32786 missing values generated)

{com}. by statecd cnrptyy rccount, sort: egen numprotestig = count(protest) if protest==1 & noorgig==0
{txt}(33380 missing values generated)

{com}. 
. gen numprotestnoig = .
{txt}(35,095 missing values generated)

{com}. replace numprotestnoig=numprotestnoorg if noorgig==1
{txt}(2,309 real changes made)

{com}. replace numprotestnoig=numprotestig if noorgig==0
{txt}(1,715 real changes made)

{com}. replace numprotestnoig=0 if protestincd==0
{txt}(29,534 real changes made)

{com}. 
. label values numprotestnoig freq
{txt}
{com}. 
. ttest numprotestnoig if support!=., by(noorgig)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Organize {c |}{res}{col 12}  1,215{col 22} 5.011523{col 34} .1315402{col 46} 4.585077{col 58} 4.753451{col 70} 5.269594
{txt}No Organ {c |}{res}{col 12}  1,781{col 22} 3.190904{col 34} .0639902{col 46} 2.700509{col 58}   3.0654{col 70} 3.316408
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,996{col 22} 3.929239{col 34} .0675113{col 46}  3.69528{col 58} 3.796866{col 70} 4.061612
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} 1.820619{col 34} .1334356{col 58} 1.558984{col 70} 2.082253
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Organize{txt}) - mean({res}No Organ{txt})                        t = {res} 13.6442
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2994

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. eststo igfreq:  melogit supportall2 i.noorgig##numprotestnoig  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}{txt}note: 8.numprotestnoig != 0 predicts success perfectly
      8.numprotestnoig dropped and 4 obs not used

note: 14.numprotestnoig != 0 predicts failure perfectly
      14.numprotestnoig dropped and 3 obs not used

note: 1.noorgig#13.numprotestnoig != 0 predicts success perfectly
      1.noorgig#13.numprotestnoig dropped and 1 obs not used

note: 0.numprotestnoig identifies no observations in the sample
note: 7.numprotestnoig identifies no observations in the sample
note: 16.numprotestnoig omitted because of collinearity
note: 17.numprotestnoig identifies no observations in the sample
note: 0.noorgig#0.numprotestnoig identifies no observations in the sample
note: 0.noorgig#7.numprotestnoig identifies no observations in the sample
note: 0.noorgig#11.numprotestnoig identifies no observations in the sample
note: 0.noorgig#17.numprotestnoig identifies no observations in the sample
note: 1.noorgig#0.numprotestnoig identifies no observations in the sample
note: 1.noorgig#5.numprotestnoig omitted because of collinearity
note: 1.noorgig#7.numprotestnoig identifies no observations in the sample
note: 1.noorgig#8.numprotestnoig identifies no observations in the sample
note: 1.noorgig#9.numprotestnoig identifies no observations in the sample
note: 1.noorgig#10.numprotestnoig identifies no observations in the sample
note: 1.noorgig#11.numprotestnoig omitted because of collinearity
note: 1.noorgig#14.numprotestnoig identifies no observations in the sample
note: 1.noorgig#16.numprotestnoig identifies no observations in the sample
note: 1.noorgig#17.numprotestnoig identifies no observations in the sample
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,277
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       212

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.7
{col 63}{txt}max{col 67}={res}{col 69}       111

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}23{txt}){col 67}={res}{col 70}   220.22
{txt}Log likelihood = {res}-1421.2973{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 38}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                          supportall2{col 39}{c |}      Coef.{col 51}   Std. Err.{col 63}      z{col 71}   P>|z|{col 79}     [95% Con{col 92}f. Interval]
{hline 38}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 37} {c |}
{space 30}noorgig {c |}
{space 9}No Organized Interest Group  {c |}{col 39}{res}{space 2}-.9782216{col 51}{space 2}  .284575{col 62}{space 1}   -3.44{col 71}{space 3}0.001{col 79}{space 4}-1.535978{col 92}{space 3}-.4204649
{txt}{space 37} {c |}
{space 23}numprotestnoig {c |}
{space 35}0  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 27}1 Protest  {c |}{col 39}{res}{space 2} .5386549{col 51}{space 2} .8678201{col 62}{space 1}    0.62{col 71}{space 3}0.535{col 79}{space 4}-1.162241{col 92}{space 3} 2.239551
{txt}{space 26}2 Protests  {c |}{col 39}{res}{space 2} 1.710797{col 51}{space 2} .9008573{col 62}{space 1}    1.90{col 71}{space 3}0.058{col 79}{space 4}-.0548509{col 92}{space 3} 3.476445
{txt}{space 26}3 Protests  {c |}{col 39}{res}{space 2}-2.266769{col 51}{space 2}   1.1468{col 62}{space 1}   -1.98{col 71}{space 3}0.048{col 79}{space 4}-4.514456{col 92}{space 3}-.0190819
{txt}{space 26}4 Protests  {c |}{col 39}{res}{space 2} .8470958{col 51}{space 2} .8912183{col 62}{space 1}    0.95{col 71}{space 3}0.342{col 79}{space 4}  -.89966{col 92}{space 3} 2.593852
{txt}{space 26}5 Protests  {c |}{col 39}{res}{space 2} .0878451{col 51}{space 2} .8879206{col 62}{space 1}    0.10{col 71}{space 3}0.921{col 79}{space 4}-1.652447{col 92}{space 3} 1.828137
{txt}{space 26}7 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 26}8 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 26}9 Protests  {c |}{col 39}{res}{space 2}-2.266695{col 51}{space 2}  .926721{col 62}{space 1}   -2.45{col 71}{space 3}0.014{col 79}{space 4}-4.083035{col 92}{space 3}-.4503557
{txt}{space 25}10 Protests  {c |}{col 39}{res}{space 2} .8016166{col 51}{space 2} 1.599564{col 62}{space 1}    0.50{col 71}{space 3}0.616{col 79}{space 4}-2.333472{col 92}{space 3} 3.936705
{txt}{space 34}11  {c |}{col 39}{res}{space 2} .6405064{col 51}{space 2} .9305276{col 62}{space 1}    0.69{col 71}{space 3}0.491{col 79}{space 4}-1.183294{col 92}{space 3} 2.464307
{txt}{space 34}13  {c |}{col 39}{res}{space 2}-.9828196{col 51}{space 2} .8795646{col 62}{space 1}   -1.12{col 71}{space 3}0.264{col 79}{space 4}-2.706734{col 92}{space 3} .7410952
{txt}{space 25}14 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 25}16 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (omitted)
{space 25}17 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 37} {c |}
{space 15}noorgig#numprotestnoig {c |}
{space 9}Organized Interest Group# 0  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 1}Organized Interest Group#7 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 1}Organized Interest Group#8 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 9}Organized Interest Group#11  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
Organized Interest Group#14 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
Organized Interest Group#17 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 6}No Organized Interest Group# 0  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 9}No Organized Interest Group #{c |}
{space 27}1 Protest  {c |}{col 39}{res}{space 2} 1.020252{col 51}{space 2} .3261383{col 62}{space 1}    3.13{col 71}{space 3}0.002{col 79}{space 4} .3810326{col 92}{space 3} 1.659471
{txt}{space 9}No Organized Interest Group #{c |}
{space 26}2 Protests  {c |}{col 39}{res}{space 2}  .021582{col 51}{space 2} .4207494{col 62}{space 1}    0.05{col 71}{space 3}0.959{col 79}{space 4}-.8030716{col 92}{space 3} .8462356
{txt}{space 9}No Organized Interest Group #{c |}
{space 26}3 Protests  {c |}{col 39}{res}{space 2} 3.402474{col 51}{space 2} .8308111{col 62}{space 1}    4.10{col 71}{space 3}0.000{col 79}{space 4} 1.774114{col 92}{space 3} 5.030834
{txt}{space 9}No Organized Interest Group #{c |}
{space 26}4 Protests  {c |}{col 39}{res}{space 2}  .225029{col 51}{space 2} .3830369{col 62}{space 1}    0.59{col 71}{space 3}0.557{col 79}{space 4}-.5257096{col 92}{space 3} .9757676
{txt}{space 9}No Organized Interest Group #{c |}
{space 26}5 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (omitted)
{space 9}No Organized Interest Group #{c |}
{space 26}7 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 9}No Organized Interest Group #{c |}
{space 26}8 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 9}No Organized Interest Group #{c |}
{space 26}9 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 9}No Organized Interest Group #{c |}
{space 25}10 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 6}No Organized Interest Group#11  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (omitted)
{space 6}No Organized Interest Group#13  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 9}No Organized Interest Group #{c |}
{space 25}14 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 9}No Organized Interest Group #{c |}
{space 25}16 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 9}No Organized Interest Group #{c |}
{space 25}17 Protests  {c |}{col 39}{res}{space 2}        0{col 51}{txt}  (empty)
{space 37} {c |}
{space 33}educ {c |}{col 39}{res}{space 2}-.4517457{col 51}{space 2} .3391657{col 62}{space 1}   -1.33{col 71}{space 3}0.183{col 79}{space 4}-1.116498{col 92}{space 3} .2130069
{txt}{space 34}dem {c |}{col 39}{res}{space 2} .5089978{col 51}{space 2} .1558993{col 62}{space 1}    3.26{col 71}{space 3}0.001{col 79}{space 4} .2034408{col 92}{space 3} .8145548
{txt}{space 26}lengthserve {c |}{col 39}{res}{space 2}-.3237368{col 51}{space 2} .4306054{col 62}{space 1}   -0.75{col 71}{space 3}0.452{col 79}{space 4}-1.167708{col 92}{space 3} .5202342
{txt}{space 27}votemargin {c |}{col 39}{res}{space 2} .7206181{col 51}{space 2} .4998558{col 62}{space 1}    1.44{col 71}{space 3}0.149{col 79}{space 4}-.2590813{col 92}{space 3} 1.700317
{txt}{space 31}blkrep {c |}{col 39}{res}{space 2}-.5788598{col 51}{space 2}  .214088{col 62}{space 1}   -2.70{col 71}{space 3}0.007{col 79}{space 4}-.9984645{col 92}{space 3}-.1592551
{txt}{space 30}hisprep {c |}{col 39}{res}{space 2}-.5202243{col 51}{space 2} .2568922{col 62}{space 1}   -2.03{col 71}{space 3}0.043{col 79}{space 4}-1.023724{col 92}{space 3}-.0167249
{txt}{space 29}womanrep {c |}{col 39}{res}{space 2}  .272162{col 51}{space 2} .1864901{col 62}{space 1}    1.46{col 71}{space 3}0.144{col 79}{space 4}-.0933519{col 92}{space 3} .6376759
{txt}{space 37} {c |}
{space 30}cnrptyy {c |}
{space 33}103  {c |}{col 39}{res}{space 2} .2491922{col 51}{space 2} .1352825{col 62}{space 1}    1.84{col 71}{space 3}0.065{col 79}{space 4}-.0159566{col 92}{space 3}  .514341
{txt}{space 33}104  {c |}{col 39}{res}{space 2} .2827592{col 51}{space 2} .1500512{col 62}{space 1}    1.88{col 71}{space 3}0.060{col 79}{space 4}-.0113357{col 92}{space 3} .5768541
{txt}{space 37} {c |}
{space 32}_cons {c |}{col 39}{res}{space 2}-.8582039{col 51}{space 2} .9416156{col 62}{space 1}   -0.91{col 71}{space 3}0.362{col 79}{space 4}-2.703737{col 92}{space 3} .9873287
{txt}{hline 38}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                              {col 39}{c |}
                            var(_cons){c |}{col 39}{res}{space 2} .2388285{col 51}{space 2} .1055399{col 79}{space 4} .1004464{col 92}{space 3} .5678554
{txt}{hline 38}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 16.26{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. margins, dydx(noorgig) at(numprotestnoig=(1/5) ) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,277
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.noorgig}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3882301 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6117699 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnoig}{space 2}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3992631 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7940272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1656311 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294466 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2402284 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1119895 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1818182 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3825209 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3838384 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2336408 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3882301 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6117699 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnoig}{space 2}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3992631 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7940272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1656311 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294466 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2402284 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1119895 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1818182 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3825209 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3838384 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2336408 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3882301 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6117699 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnoig}{space 2}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3992631 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7940272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1656311 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294466 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2402284 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1119895 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1818182 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3825209 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3838384 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2336408 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3882301 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6117699 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnoig}{space 2}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3992631 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7940272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1656311 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294466 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2402284 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1119895 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1818182 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3825209 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3838384 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2336408 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3882301 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6117699 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:numprotestnoig}{space 2}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3992631 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7940272 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1656311 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7294466 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2402284 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1119895 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1818182 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3825209 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3838384 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2336408 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.noorgig    {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0095933{col 26}{space 2} .0364322{col 37}{space 1}    0.26{col 46}{space 3}0.792{col 54}{space 4}-.0618126{col 67}{space 3} .0809992
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1763165{col 26}{space 2} .0524414{col 37}{space 1}   -3.36{col 46}{space 3}0.001{col 54}{space 4}-.2790997{col 67}{space 3}-.0735332
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4125757{col 26}{space 2} .0749518{col 37}{space 1}    5.50{col 46}{space 3}0.000{col 54}{space 4} .2656728{col 67}{space 3} .5594786
{txt}{space 10}4  {c |}{col 14}{res}{space 2} -.172681{col 26}{space 2} .0582087{col 37}{space 1}   -2.97{col 46}{space 3}0.003{col 54}{space 4}-.2867679{col 67}{space 3}-.0585942
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.2140782{col 26}{space 2} .0627447{col 37}{space 1}   -3.41{col 46}{space 3}0.001{col 54}{space 4}-.3370556{col 67}{space 3}-.0911008
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 5C ***
. marginsplot, x(numprotestnoig) graphregion(fcolor(white)) legend(off)  ///
> plotopts(mcolor(black) msize(small) lcolor(black) ) ///
> ciopts(lcolor(black)) xsize(7) ylabel(-.6(.2).6) ///
> title("(C) Resources (No Interest Group vs Interest Group)", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Number of Protests on Issue in District") yline(0, lcolor(g16))

{text}{p 2 6 2}Variables that uniquely identify margins: numprotestnoig{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g16 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. *** Appendix E, Table 8 ****
. esttab pfreq nwfreq igfreq using example.tex, booktabs scalars("ll Log likelihood") label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars ///
>                  title(Resources, Salience (Protest Frequency) and Legislative Support \label{c -(}tabfigfreq{c )-}) ///
>                  mtitles("Model A" "Model B" "Model C" ) ///
>                  indicate("Congress Year Dummies = *cnrptyy" ) ///
>                  drop(0.poorh 0.nonwhite 0.noorgig 0.poorh*numprotestp 0.nonwhite*numprotestnw 0.noorgig*numprotestnoig 1.poorh*1.numprotestp 1.nonwhite*1.numprotestnw 1.noorgig*1.numprotestnoig 1.numprotestp 1.numprotestnw 1.numprotestnoig educ  dem  lengthserve  votemargin blkrep hisprep womanrep) ///
>                  order(1.poorh 1.nonwhite 1.noorgig 2.numprotestp 3.numprotestp 4.numprotestp 5.numprotestp 6.numprotestp 1.poorh*numprotestp 1.nonwhite*numprotestnw 1.noorgig*numprotestnoig) ///
>                  addnotes("Coefficients are Log Odds. The dependent variable, legislative support, is binary taking on a value of 0 or 1. " "All independent variables are coded 0 to 1." "Estimates informing Figure 2.")
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
.                  
.                 
.         
. ******** Media Coverage *********
. ******** Media Coverage *********
. ******** Media Coverage *********
. 
. ttest stories0 if support!=., by(poorh)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Higher I {c |}{res}{col 12}  2,972{col 22} .1376178{col 34} .0063203{col 46} .3445563{col 58} .1252252{col 70} .1500103
{txt}Low-Inco {c |}{res}{col 12}     26{col 22}        0{col 34}        0{col 46}        0{col 58}        0{col 70}        0
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,998{col 22} .1364243{col 34} .0062698{col 46} .3432958{col 58} .1241308{col 70} .1487178
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1376178{col 34} .0675842{col 58} .0051016{col 70}  .270134
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Higher I{txt}) - mean({res}Low-Inco{txt})                        t = {res}  2.0362
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2996

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9791         {txt}Pr(|T| > |t|) = {res}0.0418          {txt}Pr(T > t) = {res}0.0209
{txt}
{com}. 
. eststo pstories:  melogit supportall2 i.poorh##i.stories0   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}{txt}note: 1.poorh#1.stories0 identifies no observations in the sample
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,287
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       213

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.7
{col 63}{txt}max{col 67}={res}{col 69}       111

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}    48.33
{txt}Log likelihood = {res}-1548.2322{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               supportall2{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26} {c |}
{space 21}poorh {c |}
{space 2}Low-Income Participants  {c |}{col 28}{res}{space 2} 1.662923{col 40}{space 2} .5524773{col 51}{space 1}    3.01{col 60}{space 3}0.003{col 68}{space 4} .5800871{col 81}{space 3} 2.745759
{txt}{space 26} {c |}
{space 18}stories0 {c |}
{space 8}Multiple Articles  {c |}{col 28}{res}{space 2}-.1121113{col 40}{space 2} .1250854{col 51}{space 1}   -0.90{col 60}{space 3}0.370{col 68}{space 4}-.3572742{col 81}{space 3} .1330516
{txt}{space 26} {c |}
{space 12}poorh#stories0 {c |}
{space 2}Low-Income Participants #{c |}
{space 8}Multiple Articles  {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (empty)
{space 26} {c |}
{space 22}educ {c |}{col 28}{res}{space 2}-.3993792{col 40}{space 2} .3146668{col 51}{space 1}   -1.27{col 60}{space 3}0.204{col 68}{space 4}-1.016115{col 81}{space 3} .2173564
{txt}{space 23}dem {c |}{col 28}{res}{space 2} .3903049{col 40}{space 2} .1469195{col 51}{space 1}    2.66{col 60}{space 3}0.008{col 68}{space 4}  .102348{col 81}{space 3} .6782618
{txt}{space 15}lengthserve {c |}{col 28}{res}{space 2} .0098961{col 40}{space 2} .4082019{col 51}{space 1}    0.02{col 60}{space 3}0.981{col 68}{space 4}-.7901649{col 81}{space 3} .8099572
{txt}{space 16}votemargin {c |}{col 28}{res}{space 2} .4990902{col 40}{space 2} .4673601{col 51}{space 1}    1.07{col 60}{space 3}0.286{col 68}{space 4}-.4169188{col 81}{space 3} 1.415099
{txt}{space 20}blkrep {c |}{col 28}{res}{space 2}-.5371789{col 40}{space 2} .1999173{col 51}{space 1}   -2.69{col 60}{space 3}0.007{col 68}{space 4}-.9290097{col 81}{space 3}-.1453482
{txt}{space 19}hisprep {c |}{col 28}{res}{space 2} -.452981{col 40}{space 2} .2382781{col 51}{space 1}   -1.90{col 60}{space 3}0.057{col 68}{space 4}-.9199975{col 81}{space 3} .0140356
{txt}{space 18}womanrep {c |}{col 28}{res}{space 2} .1834735{col 40}{space 2} .1721596{col 51}{space 1}    1.07{col 60}{space 3}0.287{col 68}{space 4}-.1539532{col 81}{space 3} .5209001
{txt}{space 26} {c |}
{space 19}cnrptyy {c |}
{space 22}103  {c |}{col 28}{res}{space 2} .5368254{col 40}{space 2} .1119309{col 51}{space 1}    4.80{col 60}{space 3}0.000{col 68}{space 4} .3174448{col 81}{space 3} .7562059
{txt}{space 22}104  {c |}{col 28}{res}{space 2} .5376027{col 40}{space 2} .1282558{col 51}{space 1}    4.19{col 60}{space 3}0.000{col 68}{space 4}  .286226{col 81}{space 3} .7889793
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}-.4935621{col 40}{space 2} .3472382{col 51}{space 1}   -1.42{col 60}{space 3}0.155{col 68}{space 4}-1.174137{col 81}{space 3} .1870123
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                   {col 28}{c |}
                 var(_cons){c |}{col 28}{res}{space 2} .1732866{col 40}{space 2} .0953346{col 68}{space 4} .0589484{col 81}{space 3} .5093991
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 9.14{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0012
{txt}
{com}. margins, dydx(poorh) over(stories0) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,287
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.poorh}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:over}:{space 1}{res:stories0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt}0.stories0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:0.poorh}{space 9}{txt:=} {space 3}.9867211 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:1.poorh}{space 9}{txt:=} {space 3}.0132789 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:stories0}{space 8}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:educ}{space 12}{txt:=} {space 3}.3977432 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:dem}{space 13}{txt:=} {space 3}.7849847 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:lengthserve}{space 5}{txt:=} {space 3}.1675717 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:votemargin}{space 6}{txt:=} {space 3}.7282584 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:blkrep}{space 10}{txt:=} {space 3}.2354443 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:hisprep}{space 9}{txt:=} {space 3}.1072523 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:womanrep}{space 8}{txt:=} {space 3}.1762002 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3896834 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3564862 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2538304 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt}1.stories0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:poorh}{space 11}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:stories0}{space 8}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:educ}{space 12}{txt:=} {space 3}.4085395 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:dem}{space 13}{txt:=} {space 3}.8358663 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:lengthserve}{space 5}{txt:=} {space 3}.1586359 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:votemargin}{space 6}{txt:=} {space 3}.7341945 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:blkrep}{space 10}{txt:=} {space 3}.2613982 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:hisprep}{space 9}{txt:=} {space 3}.1367781 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:womanrep}{space 8}{txt:=} {space 3}.2097264 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.343465 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:103.cnrptyy}{space 5}{txt:=} {space 4}.550152 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{space 4}{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.106383 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32} Delta-method
{col 20}{c |}      dy/dx{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.poorh            {txt}{c |}
{space 10}stories0 {c |}
{space 6}One Article  {c |}{col 20}{res}{space 2} .3100436{col 32}{space 2}  .066459{col 43}{space 1}    4.67{col 52}{space 3}0.000{col 60}{space 4} .1797864{col 73}{space 3} .4403008
{txt}Multiple Articles  {c |}{col 20}{res}{space 2}        .{col 32}{txt}  (not estimable)
{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 84}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 6A ***
. marginsplot, x(stories0) xlabel(0 "no" 1 "yes") graphregion(fcolor(white)) legend(off)  ///
> plotopts(color(gray) msize(small) lcolor(black) ) recast(bar) ///
> ciopts(lcolor(black) lpattern(dash)) ylabel(-.6(.2).6) ///
> title(" (A) Resources (Poor vs Nonpoor)", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Multiple Newspaper Articles on Event") yline(0, lcolor(g8)) 

{text}{p 2 6 2}Variables that uniquely identify margins: stories0{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g8 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}.  
. 
. ttest stories0 if support!=., by(nonwhite)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
White Pa {c |}{res}{col 12}  2,457{col 22} .1461131{col 34} .0071274{col 46} .3532915{col 58} .1321368{col 70} .1600895
{txt}Nonwhite {c |}{res}{col 12}    541{col 22} .0924214{col 34} .0124633{col 46}  .289888{col 58}  .067939{col 70} .1169039
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,998{col 22} .1364243{col 34} .0062698{col 46} .3432958{col 58} .1241308{col 70} .1487178
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0536917{col 34} .0162768{col 58} .0217769{col 70} .0856065
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}White Pa{txt}) - mean({res}Nonwhite{txt})                        t = {res}  3.2987
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2996

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9995         {txt}Pr(|T| > |t|) = {res}0.0010          {txt}Pr(T > t) = {res}0.0005
{txt}
{com}. 
. eststo nwstories:  melogit supportall2 nonwhite##stories0   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,287
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       213

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.7
{col 63}{txt}max{col 67}={res}{col 69}       111

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}    61.19
{txt}Log likelihood = {res}-1540.2275{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}             supportall2{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24} {c |}
{space 16}nonwhite {c |}
{space 2}Nonwhite Participants  {c |}{col 26}{res}{space 2} .2568775{col 38}{space 2} .1220441{col 49}{space 1}    2.10{col 58}{space 3}0.035{col 66}{space 4} .0176755{col 79}{space 3} .4960796
{txt}{space 24} {c |}
{space 16}stories0 {c |}
{space 6}Multiple Articles  {c |}{col 26}{res}{space 2}-.3181319{col 38}{space 2} .1360398{col 49}{space 1}   -2.34{col 58}{space 3}0.019{col 66}{space 4} -.584765{col 79}{space 3}-.0514989
{txt}{space 24} {c |}
{space 7}nonwhite#stories0 {c |}
{space 2}Nonwhite Participants #{c |}
{space 6}Multiple Articles  {c |}{col 26}{res}{space 2} 1.565031{col 38}{space 2}  .431032{col 49}{space 1}    3.63{col 58}{space 3}0.000{col 66}{space 4} .7202236{col 79}{space 3} 2.409838
{txt}{space 24} {c |}
{space 20}educ {c |}{col 26}{res}{space 2}-.4100983{col 38}{space 2} .3160479{col 49}{space 1}   -1.30{col 58}{space 3}0.194{col 66}{space 4}-1.029541{col 79}{space 3} .2093442
{txt}{space 21}dem {c |}{col 26}{res}{space 2} .3948692{col 38}{space 2} .1472197{col 49}{space 1}    2.68{col 58}{space 3}0.007{col 66}{space 4} .1063239{col 79}{space 3} .6834146
{txt}{space 13}lengthserve {c |}{col 26}{res}{space 2}-.0064585{col 38}{space 2}  .408955{col 49}{space 1}   -0.02{col 58}{space 3}0.987{col 66}{space 4}-.8079956{col 79}{space 3} .7950786
{txt}{space 14}votemargin {c |}{col 26}{res}{space 2} .5881501{col 38}{space 2} .4677344{col 49}{space 1}    1.26{col 58}{space 3}0.209{col 66}{space 4}-.3285926{col 79}{space 3} 1.504893
{txt}{space 18}blkrep {c |}{col 26}{res}{space 2}-.5752308{col 38}{space 2} .2018942{col 49}{space 1}   -2.85{col 58}{space 3}0.004{col 66}{space 4}-.9709362{col 79}{space 3}-.1795254
{txt}{space 17}hisprep {c |}{col 26}{res}{space 2}-.5078179{col 38}{space 2} .2407763{col 49}{space 1}   -2.11{col 58}{space 3}0.035{col 66}{space 4}-.9797307{col 79}{space 3} -.035905
{txt}{space 16}womanrep {c |}{col 26}{res}{space 2} .1833566{col 38}{space 2} .1725952{col 49}{space 1}    1.06{col 58}{space 3}0.288{col 66}{space 4}-.1549237{col 79}{space 3}  .521637
{txt}{space 24} {c |}
{space 17}cnrptyy {c |}
{space 20}103  {c |}{col 26}{res}{space 2} .5320381{col 38}{space 2} .1125547{col 49}{space 1}    4.73{col 58}{space 3}0.000{col 66}{space 4}  .311435{col 79}{space 3} .7526412
{txt}{space 20}104  {c |}{col 26}{res}{space 2}  .410235{col 38}{space 2} .1315868{col 49}{space 1}    3.12{col 58}{space 3}0.002{col 66}{space 4} .1523296{col 79}{space 3} .6681404
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-.5643967{col 38}{space 2} .3490954{col 49}{space 1}   -1.62{col 58}{space 3}0.106{col 66}{space 4}-1.248611{col 79}{space 3} .1198178
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                 {col 26}{c |}
               var(_cons){c |}{col 26}{res}{space 2} .1760678{col 38}{space 2} .0990836{col 66}{space 4} .0584332{col 79}{space 3} .5305187
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 8.71{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0016
{txt}
{com}. margins, dydx(nonwhite) at(stories0=(0 1)) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,287
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.nonwhite}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7944906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2055094 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:stories0}{space 8}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3992963 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7923043 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1662863 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7291124 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 4}.239178 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1114998 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1810232 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3830345 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3843463 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2326192 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 3}.7944906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 3}.2055094 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:stories0}{space 8}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3992963 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7923043 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1662863 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7291124 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 4}.239178 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1114998 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1810232 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3830345 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3843463 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2326192 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.nonwhite   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0604862{col 26}{space 2} .0284116{col 37}{space 1}    2.13{col 46}{space 3}0.033{col 54}{space 4} .0048005{col 67}{space 3} .1161719
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3716729{col 26}{space 2} .0625211{col 37}{space 1}    5.94{col 46}{space 3}0.000{col 54}{space 4} .2491337{col 67}{space 3} .4942121
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 6B ***
. marginsplot, x(stories0) xlabel(0 "no" 1 "yes") graphregion(fcolor(white)) legend(off)  ///
> plotopts(color(gray) msize(small) lcolor(black) ) recast(bar) ///
> ciopts(lcolor(black) lpattern(dash)) ylabel(-.6(.2).6) ///
> title("(B) Resources (Nonwhite vs White)", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Multiple Newspaper Articles on Event") yline(0, lcolor(g8)) 

{text}{p 2 6 2}Variables that uniquely identify margins: stories0{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g8 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. 
. ttest stories0 if support!=., by(noorgig)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Organize {c |}{res}{col 12}  1,201{col 22} .1407161{col 34} .0100381{col 46} .3478733{col 58}  .121022{col 70} .1604102
{txt}No Organ {c |}{res}{col 12}  1,781{col 22} .1263335{col 34} .0078745{col 46} .3323182{col 58} .1108893{col 70} .1417777
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,982{col 22} .1321261{col 34} .0062021{col 46} .3386846{col 58} .1199652{col 70}  .144287
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0143825{col 34} .0126452{col 58}-.0104116{col 70} .0391767
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Organize{txt}) - mean({res}No Organ{txt})                        t = {res}  1.1374
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2980

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.8723         {txt}Pr(|T| > |t|) = {res}0.2555          {txt}Pr(T > t) = {res}0.1277
{txt}
{com}. 
. eststo igstories:  melogit supportall2 noorgig##stories0   educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,271
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       212

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.7
{col 63}{txt}max{col 67}={res}{col 69}       110

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}    47.17
{txt}Log likelihood = {res}-1539.9492{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                   supportall2{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 30} {c |}
{space 23}noorgig {c |}
{space 2}No Organized Interest Group  {c |}{col 32}{res}{space 2} .0111272{col 44}{space 2} .0977895{col 55}{space 1}    0.11{col 64}{space 3}0.909{col 72}{space 4}-.1805367{col 85}{space 3} .2027912
{txt}{space 30} {c |}
{space 22}stories0 {c |}
{space 12}Multiple Articles  {c |}{col 32}{res}{space 2}-.6400466{col 44}{space 2} .2098248{col 55}{space 1}   -3.05{col 64}{space 3}0.002{col 72}{space 4}-1.051296{col 85}{space 3}-.2287976
{txt}{space 30} {c |}
{space 14}noorgig#stories0 {c |}
{space 2}No Organized Interest Group #{c |}
{space 12}Multiple Articles  {c |}{col 32}{res}{space 2} .7108538{col 44}{space 2}   .26315{col 55}{space 1}    2.70{col 64}{space 3}0.007{col 72}{space 4} .1950892{col 85}{space 3} 1.226618
{txt}{space 30} {c |}
{space 26}educ {c |}{col 32}{res}{space 2}-.3964641{col 44}{space 2} .3153623{col 55}{space 1}   -1.26{col 64}{space 3}0.209{col 72}{space 4}-1.014563{col 85}{space 3} .2216347
{txt}{space 27}dem {c |}{col 32}{res}{space 2} .3990512{col 44}{space 2} .1474951{col 55}{space 1}    2.71{col 64}{space 3}0.007{col 72}{space 4} .1099661{col 85}{space 3} .6881363
{txt}{space 19}lengthserve {c |}{col 32}{res}{space 2}-.0754219{col 44}{space 2} .4114326{col 55}{space 1}   -0.18{col 64}{space 3}0.855{col 72}{space 4}-.8818149{col 85}{space 3} .7309712
{txt}{space 20}votemargin {c |}{col 32}{res}{space 2} .5980999{col 44}{space 2} .4678555{col 55}{space 1}    1.28{col 64}{space 3}0.201{col 72}{space 4}-.3188799{col 85}{space 3}  1.51508
{txt}{space 24}blkrep {c |}{col 32}{res}{space 2}-.5485444{col 44}{space 2} .2003372{col 55}{space 1}   -2.74{col 64}{space 3}0.006{col 72}{space 4} -.941198{col 85}{space 3}-.1558907
{txt}{space 23}hisprep {c |}{col 32}{res}{space 2}-.4831633{col 44}{space 2} .2391043{col 55}{space 1}   -2.02{col 64}{space 3}0.043{col 72}{space 4}-.9517991{col 85}{space 3}-.0145275
{txt}{space 22}womanrep {c |}{col 32}{res}{space 2} .1825495{col 44}{space 2} .1717852{col 55}{space 1}    1.06{col 64}{space 3}0.288{col 72}{space 4}-.1541433{col 85}{space 3} .5192423
{txt}{space 30} {c |}
{space 23}cnrptyy {c |}
{space 26}103  {c |}{col 32}{res}{space 2} .5350196{col 44}{space 2} .1125978{col 55}{space 1}    4.75{col 64}{space 3}0.000{col 72}{space 4}  .314332{col 85}{space 3} .7557073
{txt}{space 26}104  {c |}{col 32}{res}{space 2}  .496919{col 44}{space 2} .1293912{col 55}{space 1}    3.84{col 64}{space 3}0.000{col 72}{space 4} .2433169{col 85}{space 3} .7505212
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2}-.5418824{col 44}{space 2} .3552683{col 55}{space 1}   -1.53{col 64}{space 3}0.127{col 72}{space 4}-1.238195{col 85}{space 3} .1544307
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                       {col 32}{c |}
                     var(_cons){c |}{col 32}{res}{space 2} .1707942{col 44}{space 2} .0958765{col 72}{space 4} .0568389{col 85}{space 3}  .513217
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 8.85{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0015
{txt}
{com}. margins, dydx(noorgig) at(stories0=(0 1)) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,271
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.noorgig}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3861735 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6138265 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:stories0}{space 8}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3988419 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 4}.792162 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1664352 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7290004 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2391017 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1114047 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1805372 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3852928 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3804491 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.234258 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3861735 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6138265 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:stories0}{space 8}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3988419 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 4}.792162 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1664352 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7290004 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2391017 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1114047 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1805372 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3852928 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3804491 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 4}.234258 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.noorgig    {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0026453{col 26}{space 2} .0232481{col 37}{space 1}    0.11{col 46}{space 3}0.909{col 54}{space 4}-.0429202{col 67}{space 3} .0482107
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1715295{col 26}{space 2} .0569614{col 37}{space 1}    3.01{col 46}{space 3}0.003{col 54}{space 4} .0598871{col 67}{space 3} .2831718
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 6C ***
. marginsplot, x(stories0) xlabel(0 "no" 1 "yes") graphregion(fcolor(white)) legend(off)  ///
> plotopts(color(gray) msize(small) lcolor(black) ) recast(bar) ///
> ciopts(lcolor(black) lpattern(dash)) ylabel(-.6(.2).6) ///
> title("(C) Organizational Resources", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Multiple Newspaper Articles on Event") yline(0, lcolor(g8))  

{text}{p 2 6 2}Variables that uniquely identify margins: stories0{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g8 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. *** Appendix E, Table 9 ****
. esttab pstories nwstories igstories using example.tex, booktabs scalars("ll Log likelihood") label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars ///
>                  title(Resources, Salience (Media Coverage) and Legislative Support \label{c -(}tabfigmed{c )-}) ///
>                  mtitles("Model A" "Model B" "Model C" "Model D") ///
>                  indicate("Congress Year Dummies = *cnrptyy" ) ///
>                  drop(0.poorh 0.nonwhite 0.noorgig 0.poorh*stories0 0.nonwhite*stories0 0.noorgig*stories0  1.poorh*0.stories0 1.nonwhite*0.stories0 1.noorgig*0.stories0 0.stories0 educ  dem  lengthserve  votemargin blkrep hisprep womanrep) ///
>                  order(1.poorh 1.nonwhite 1.noorgig 1.stories0 1.poorh*stories0 1.nonwhite*stories0 1.noorgig*stories0) ///
>                  addnotes("Coefficients are Log Odds. The dependent variable, legislative support, is binary taking on a value of 0 or 1. " "All independent variables are coded 0 to 1." "Estimates informing Figure 4.")
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
. 
. **** Disruptiveness ****
. **** Disruptiveness ****
. **** Disruptiveness ****
. 
. 
. ttest disruptive0 if support!=., by(poorh)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Higher I {c |}{res}{col 12}  2,986{col 22} .3004019{col 34} .0083908{col 46} .4585095{col 58} .2839495{col 70} .3168542
{txt}Low-Inco {c |}{res}{col 12}     26{col 22}        0{col 34}        0{col 46}        0{col 58}        0{col 70}        0
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  3,012{col 22} .2978088{col 34} .0083338{col 46} .4573709{col 58} .2814683{col 70} .3141492
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .3004019{col 34} .0899359{col 58} .1240598{col 70}  .476744
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Higher I{txt}) - mean({res}Low-Inco{txt})                        t = {res}  3.3402
{txt}Ho: diff = 0                                     degrees of freedom = {res}    3010

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9996         {txt}Pr(|T| > |t|) = {res}0.0008          {txt}Pr(T > t) = {res}0.0004
{txt}
{com}. 
. eststo pdis:  melogit supportall2 poorh##disruptive0    educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}{txt}note: 1.poorh#1.disruptive0 identifies no observations in the sample
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,301
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       213

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.8
{col 63}{txt}max{col 67}={res}{col 69}       112

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}    46.34
{txt}Log likelihood = {res}-1559.7875{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                         supportall2{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      z{col 70}   P>|z|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 36} {c |}
{space 31}poorh {c |}
{space 12}Low-Income Participants  {c |}{col 38}{res}{space 2} 1.666989{col 50}{space 2} .5526804{col 61}{space 1}    3.02{col 70}{space 3}0.003{col 78}{space 4} .5837555{col 91}{space 3} 2.750223
{txt}{space 36} {c |}
{space 25}disruptive0 {c |}
{space 25}Disruptive  {c |}{col 38}{res}{space 2}-.0938561{col 50}{space 2} .0981071{col 61}{space 1}   -0.96{col 70}{space 3}0.339{col 78}{space 4}-.2861426{col 91}{space 3} .0984303
{txt}{space 36} {c |}
{space 19}poorh#disruptive0 {c |}
{space 1}Low-Income Participants#Disruptive  {c |}{col 38}{res}{space 2}        0{col 50}{txt}  (empty)
{space 36} {c |}
{space 32}educ {c |}{col 38}{res}{space 2}-.4189836{col 50}{space 2} .3136178{col 61}{space 1}   -1.34{col 70}{space 3}0.182{col 78}{space 4}-1.033663{col 91}{space 3}  .195696
{txt}{space 33}dem {c |}{col 38}{res}{space 2} .3781346{col 50}{space 2} .1467214{col 61}{space 1}    2.58{col 70}{space 3}0.010{col 78}{space 4}  .090566{col 91}{space 3} .6657031
{txt}{space 25}lengthserve {c |}{col 38}{res}{space 2} .0092239{col 50}{space 2} .4077493{col 61}{space 1}    0.02{col 70}{space 3}0.982{col 78}{space 4}  -.78995{col 91}{space 3} .8083978
{txt}{space 26}votemargin {c |}{col 38}{res}{space 2} .4765235{col 50}{space 2} .4662153{col 61}{space 1}    1.02{col 70}{space 3}0.307{col 78}{space 4}-.4372417{col 91}{space 3} 1.390289
{txt}{space 30}blkrep {c |}{col 38}{res}{space 2}-.5421086{col 50}{space 2} .1993335{col 61}{space 1}   -2.72{col 70}{space 3}0.007{col 78}{space 4}-.9327952{col 91}{space 3}-.1514221
{txt}{space 29}hisprep {c |}{col 38}{res}{space 2}-.4624746{col 50}{space 2} .2376388{col 61}{space 1}   -1.95{col 70}{space 3}0.052{col 78}{space 4}-.9282381{col 91}{space 3} .0032888
{txt}{space 28}womanrep {c |}{col 38}{res}{space 2} .1784697{col 50}{space 2}   .17142{col 61}{space 1}    1.04{col 70}{space 3}0.298{col 78}{space 4}-.1575072{col 91}{space 3} .5144466
{txt}{space 36} {c |}
{space 29}cnrptyy {c |}
{space 32}103  {c |}{col 38}{res}{space 2} .4835882{col 50}{space 2} .1118174{col 61}{space 1}    4.32{col 70}{space 3}0.000{col 78}{space 4}   .26443{col 91}{space 3} .7027463
{txt}{space 32}104  {c |}{col 38}{res}{space 2} .5230791{col 50}{space 2} .1303601{col 61}{space 1}    4.01{col 70}{space 3}0.000{col 78}{space 4} .2675779{col 91}{space 3} .7785802
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2}-.4298228{col 50}{space 2} .3478421{col 61}{space 1}   -1.24{col 70}{space 3}0.217{col 78}{space 4}-1.111581{col 91}{space 3} .2519352
{txt}{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                             {col 38}{c |}
                           var(_cons){c |}{col 38}{res}{space 2} .1733475{col 50}{space 2} .0941182{col 78}{space 4} .0598083{col 91}{space 3} .5024275
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 9.53{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0010
{txt}
{com}. margins, dydx(poorh) at(disruptive0=(0 1)) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,301
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.poorh}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9887006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0112994 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:disruptive0}{space 5}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.poorh}{space 9}{txt:=} {space 3}.9887006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.poorh}{space 9}{txt:=} {space 3}.0112994 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:disruptive0}{space 5}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.poorh      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .3096767{col 26}{space 2} .0661695{col 37}{space 1}    4.68{col 46}{space 3}0.000{col 54}{space 4}  .179987{col 67}{space 3} .4393665
{txt}{space 10}2  {c |}{col 14}{res}{space 2}        .{col 26}{txt}  (not estimable)
{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 7A ***
. marginsplot, x(disruptive0) xlabel(0 "no" 1 "yes") graphregion(fcolor(white)) legend(off)  ///
> plotopts(color(gray) msize(small) lcolor(black) ) recast(bar) ///
> ciopts(lcolor(black) lpattern(dash)) ylabel(-.2(.1).6)  ///
> title("(A) Resources (Poor vs Nonpoor)", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Disruptive Event") yline(0, lcolor(g8))

{text}{p 2 6 2}Variables that uniquely identify margins: disruptive0{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g8 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. ttest disruptive0 if support!=., by(nonwhite)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
White Pa {c |}{res}{col 12}  2,471{col 22} .2986645{col 34} .0092089{col 46}  .457765{col 58} .2806066{col 70} .3167224
{txt}Nonwhite {c |}{res}{col 12}    541{col 22} .2939002{col 34} .0196036{col 46} .4559684{col 58} .2553915{col 70} .3324089
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  3,012{col 22} .2978088{col 34} .0083338{col 46} .4573709{col 58} .2814683{col 70} .3141492
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0047643{col 34} .0217135{col 58}-.0378105{col 70} .0473391
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}White Pa{txt}) - mean({res}Nonwhite{txt})                        t = {res}  0.2194
{txt}Ho: diff = 0                                     degrees of freedom = {res}    3010

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.5868         {txt}Pr(|T| > |t|) = {res}0.8263          {txt}Pr(T > t) = {res}0.4132
{txt}
{com}. 
. eststo nwdis:  melogit supportall2 nonwhite##disruptive0    educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,301
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       213

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.8
{col 63}{txt}max{col 67}={res}{col 69}       112

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}    57.29
{txt}Log likelihood = {res}-1554.8503{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                       supportall2{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 34} {c |}
{space 26}nonwhite {c |}
{space 12}Nonwhite Participants  {c |}{col 36}{res}{space 2} .2230101{col 48}{space 2}  .135146{col 59}{space 1}    1.65{col 68}{space 3}0.099{col 76}{space 4}-.0418712{col 89}{space 3} .4878914
{txt}{space 34} {c |}
{space 23}disruptive0 {c |}
{space 23}Disruptive  {c |}{col 36}{res}{space 2}-.2756495{col 48}{space 2} .1091975{col 59}{space 1}   -2.52{col 68}{space 3}0.012{col 76}{space 4}-.4896727{col 89}{space 3}-.0616262
{txt}{space 34} {c |}
{space 14}nonwhite#disruptive0 {c |}
{space 1}Nonwhite Participants#Disruptive  {c |}{col 36}{res}{space 2} .7329202{col 48}{space 2} .2556988{col 59}{space 1}    2.87{col 68}{space 3}0.004{col 76}{space 4} .2317598{col 89}{space 3} 1.234081
{txt}{space 34} {c |}
{space 30}educ {c |}{col 36}{res}{space 2}-.4384486{col 48}{space 2} .3129148{col 59}{space 1}   -1.40{col 68}{space 3}0.161{col 76}{space 4} -1.05175{col 89}{space 3} .1748531
{txt}{space 31}dem {c |}{col 36}{res}{space 2} .3742757{col 48}{space 2} .1460894{col 59}{space 1}    2.56{col 68}{space 3}0.010{col 76}{space 4} .0879457{col 89}{space 3} .6606058
{txt}{space 23}lengthserve {c |}{col 36}{res}{space 2}-.0256437{col 48}{space 2} .4061475{col 59}{space 1}   -0.06{col 68}{space 3}0.950{col 76}{space 4}-.8216781{col 89}{space 3} .7703907
{txt}{space 24}votemargin {c |}{col 36}{res}{space 2} .5734244{col 48}{space 2}  .462834{col 59}{space 1}    1.24{col 68}{space 3}0.215{col 76}{space 4}-.3337137{col 89}{space 3} 1.480562
{txt}{space 28}blkrep {c |}{col 36}{res}{space 2}-.5781621{col 48}{space 2} .1993536{col 59}{space 1}   -2.90{col 68}{space 3}0.004{col 76}{space 4} -.968888{col 89}{space 3}-.1874362
{txt}{space 27}hisprep {c |}{col 36}{res}{space 2}-.5178033{col 48}{space 2} .2371069{col 59}{space 1}   -2.18{col 68}{space 3}0.029{col 76}{space 4}-.9825243{col 89}{space 3}-.0530823
{txt}{space 26}womanrep {c |}{col 36}{res}{space 2} .1730949{col 48}{space 2}  .169614{col 59}{space 1}    1.02{col 68}{space 3}0.307{col 76}{space 4}-.1593424{col 89}{space 3} .5055322
{txt}{space 34} {c |}
{space 27}cnrptyy {c |}
{space 30}103  {c |}{col 36}{res}{space 2} .4050262{col 48}{space 2} .1129703{col 59}{space 1}    3.59{col 68}{space 3}0.000{col 76}{space 4} .1836084{col 89}{space 3} .6264439
{txt}{space 30}104  {c |}{col 36}{res}{space 2} .3869453{col 48}{space 2}  .133281{col 59}{space 1}    2.90{col 68}{space 3}0.004{col 76}{space 4} .1257194{col 89}{space 3} .6481713
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2}-.4432337{col 48}{space 2} .3476522{col 59}{space 1}   -1.27{col 68}{space 3}0.202{col 76}{space 4}-1.124619{col 89}{space 3} .2381521
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                           {col 36}{c |}
                         var(_cons){c |}{col 36}{res}{space 2} .1612847{col 48}{space 2} .0925824{col 76}{space 4} .0523573{col 89}{space 3} .4968315
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 8.37{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0019
{txt}
{com}. margins, dydx(nonwhite) at(disruptive0=(0 1)) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,301
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.nonwhite}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 4}.795741 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 4}.204259 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:disruptive0}{space 5}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.nonwhite}{space 6}{txt:=} {space 4}.795741 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.nonwhite}{space 6}{txt:=} {space 4}.204259 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:disruptive0}{space 5}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3996013 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7931334 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1660509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 3}.7292612 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2394611 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1116906 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1816601 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 4}.380704 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3880921 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2312038 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.nonwhite   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0526523{col 26}{space 2} .0315689{col 37}{space 1}    1.67{col 46}{space 3}0.095{col 54}{space 4}-.0092215{col 67}{space 3} .1145262
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2194843{col 26}{space 2} .0470554{col 37}{space 1}    4.66{col 46}{space 3}0.000{col 54}{space 4} .1272573{col 67}{space 3} .3117113
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 7B ***
. marginsplot, x(disruptive0) xlabel(0 "no" 1 "yes") graphregion(fcolor(white)) legend(off)  ///
> plotopts(color(gray) msize(small) lcolor(black) ) recast(bar) ///
> ciopts(lcolor(black) lpattern(dash)) ylabel(-.2(.1).6)  ///
> title("(B) Resources (Nonwhite vs White)", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Disruptive Event") yline(0, lcolor(g8))

{text}{p 2 6 2}Variables that uniquely identify margins: disruptive0{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
g8 not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. ttest disruptive0 if support!=., by(noorgig)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Organize {c |}{res}{col 12}  1,215{col 22} .1786008{col 34} .0109928{col 46} .3831754{col 58} .1570338{col 70} .2001679
{txt}No Organ {c |}{res}{col 12}  1,781{col 22} .3733857{col 34} .0114649{col 46} .4838391{col 58} .3508997{col 70} .3958718
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,996{col 22} .2943925{col 34} .0083281{col 46} .4558453{col 58} .2780631{col 70} .3107219
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.1947849{col 34} .0165868{col 58}-.2273076{col 70}-.1622623
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Organize{txt}) - mean({res}No Organ{txt})                        t = {res}-11.7434
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2994

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. 
. eststo igdis:  melogit supportall2 noorgig##disruptive0    educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if lib==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,285
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       212

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      10.8
{col 63}{txt}max{col 67}={res}{col 69}       111

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}    39.98
{txt}Log likelihood = {res}-1554.1159{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0001
{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                   supportall2{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 30} {c |}
{space 23}noorgig {c |}
{space 2}No Organized Interest Group  {c |}{col 32}{res}{space 2} .1954544{col 44}{space 2} .1039354{col 55}{space 1}    1.88{col 64}{space 3}0.060{col 72}{space 4}-.0082553{col 85}{space 3} .3991642
{txt}{space 30} {c |}
{space 19}disruptive0 {c |}
{space 19}Disruptive  {c |}{col 32}{res}{space 2}-.1214228{col 44}{space 2} .1906506{col 55}{space 1}   -0.64{col 64}{space 3}0.524{col 72}{space 4}-.4950911{col 85}{space 3} .2522455
{txt}{space 30} {c |}
{space 11}noorgig#disruptive0 {c |}
{space 2}No Organized Interest Group #{c |}
{space 19}Disruptive  {c |}{col 32}{res}{space 2}-.0907438{col 44}{space 2} .2239244{col 55}{space 1}   -0.41{col 64}{space 3}0.685{col 72}{space 4}-.5296276{col 85}{space 3} .3481401
{txt}{space 30} {c |}
{space 26}educ {c |}{col 32}{res}{space 2}-.4273309{col 44}{space 2} .3148526{col 55}{space 1}   -1.36{col 64}{space 3}0.175{col 72}{space 4}-1.044431{col 85}{space 3} .1897688
{txt}{space 27}dem {c |}{col 32}{res}{space 2} .3946343{col 44}{space 2} .1480751{col 55}{space 1}    2.67{col 64}{space 3}0.008{col 72}{space 4} .1044125{col 85}{space 3} .6848562
{txt}{space 19}lengthserve {c |}{col 32}{res}{space 2}-.0779287{col 44}{space 2} .4114573{col 55}{space 1}   -0.19{col 64}{space 3}0.850{col 72}{space 4}-.8843703{col 85}{space 3} .7285128
{txt}{space 20}votemargin {c |}{col 32}{res}{space 2} .5637771{col 44}{space 2} .4677946{col 55}{space 1}    1.21{col 64}{space 3}0.228{col 72}{space 4}-.3530835{col 85}{space 3} 1.480638
{txt}{space 24}blkrep {c |}{col 32}{res}{space 2}-.5654895{col 44}{space 2} .2005026{col 55}{space 1}   -2.82{col 64}{space 3}0.005{col 72}{space 4}-.9584674{col 85}{space 3}-.1725116
{txt}{space 23}hisprep {c |}{col 32}{res}{space 2}-.4999261{col 44}{space 2}   .23936{col 55}{space 1}   -2.09{col 64}{space 3}0.037{col 72}{space 4} -.969063{col 85}{space 3}-.0307891
{txt}{space 22}womanrep {c |}{col 32}{res}{space 2} .1727945{col 44}{space 2} .1720714{col 55}{space 1}    1.00{col 64}{space 3}0.315{col 72}{space 4}-.1644592{col 85}{space 3} .5100482
{txt}{space 30} {c |}
{space 23}cnrptyy {c |}
{space 26}103  {c |}{col 32}{res}{space 2} .4476386{col 44}{space 2} .1132084{col 55}{space 1}    3.95{col 64}{space 3}0.000{col 72}{space 4} .2257542{col 85}{space 3}  .669523
{txt}{space 26}104  {c |}{col 32}{res}{space 2} .5035677{col 44}{space 2} .1307769{col 55}{space 1}    3.85{col 64}{space 3}0.000{col 72}{space 4} .2472496{col 85}{space 3} .7598858
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2}-.5507223{col 44}{space 2} .3564672{col 55}{space 1}   -1.54{col 64}{space 3}0.122{col 72}{space 4}-1.249385{col 85}{space 3} .1479407
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                       {col 32}{c |}
                     var(_cons){c |}{col 32}{res}{space 2} .1784758{col 44}{space 2}  .096577{col 72}{space 4} .0617983{col 85}{space 3} .5154449
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 9.70{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0009
{txt}
{com}. margins, dydx(noorgig) at(disruptive0=(0 1)) vsquish atmeans 
{res}
{txt}Conditional marginal effects{col 49}Number of obs{col 67}= {res}     2,285
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.noorgig}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3899344 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6100656 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:disruptive0}{space 5}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3991518 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7929978 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1661973 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 4}.729151 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2393873 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1115974 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1811816 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3829322 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3842451 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2328228 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:0.noorgig}{space 7}{txt:=} {space 3}.3899344 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.noorgig}{space 7}{txt:=} {space 3}.6100656 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:disruptive0}{space 5}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:educ}{space 12}{txt:=} {space 3}.3991518 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:dem}{space 13}{txt:=} {space 3}.7929978 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:lengthserve}{space 5}{txt:=} {space 3}.1661973 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:votemargin}{space 6}{txt:=} {space 4}.729151 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:blkrep}{space 10}{txt:=} {space 3}.2393873 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:hisprep}{space 9}{txt:=} {space 3}.1115974 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:womanrep}{space 8}{txt:=} {space 3}.1811816 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:102.cnrptyy}{space 5}{txt:=} {space 3}.3829322 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:103.cnrptyy}{space 5}{txt:=} {space 3}.3842451 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:104.cnrptyy}{space 5}{txt:=} {space 3}.2328228 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.noorgig    {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}   .04634{col 26}{space 2} .0245983{col 37}{space 1}    1.88{col 46}{space 3}0.060{col 54}{space 4}-.0018718{col 67}{space 3} .0945517
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0250835{col 26}{space 2} .0476648{col 37}{space 1}    0.53{col 46}{space 3}0.599{col 54}{space 4}-.0683378{col 67}{space 3} .1185048
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *** Figure 7C ***
. marginsplot, x(disruptive0) xlabel(0 "no" 1 "yes") graphregion(fcolor(white)) legend(off)  ///
> plotopts(color(gray) msize(small) lcolor(black) ) recast(bar) ///
> ciopts(lcolor(black) lpattern(dash)) ylabel(-.2(.1).6)  ///
> title("(C) Organizational Resources", color(black))  ///
> ytitle("Difference in Effect of Resources") xtitle("Disruptive Event")

{text}{p 2 6 2}Variables that uniquely identify margins: disruptive0{p_end}
{res}{txt}
{com}. 
. *** Appendix E, Table 10 ****
. esttab pdis nwdis igdis using example.tex, booktabs scalars("ll Log likelihood") label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars ///
>                  title(Resources, Salience (Disruptiveness) and Legislative Support \label{c -(}tabfigdis{c )-}) ///
>                  mtitles("Model A" "Model B" "Model C") ///
>                  indicate("Congress Year Dummies = *cnrptyy" ) ///
>                  drop(0.poorh 0.nonwhite 0.noorgig 0.poorh*disruptive0 0.nonwhite*disruptive0 0.noorgig*disruptive0  1.poorh*0.disruptive0 1.nonwhite*0.disruptive0 1.noorgig*0.disruptive0 0.disruptive0 educ  dem  lengthserve  votemargin blkrep hisprep womanrep) ///
>                  order(1.poorh 1.nonwhite 1.noorgig 1.disruptive0 1.poorh*disruptive0 1.nonwhite*disruptive0 1.noorgig*disruptive0) ///
>                  addnotes("Coefficients are Log Odds. The dependent variable, legislative support, is binary taking on a value of 0 or 1. " "All independent variables are coded 0 to 1." "Estimates informing Figure 5.")
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
. 
. *************************************
. *************************************
. ******** Robustness Checks  *********
. *************************************
. *************************************
. 
. ****** LPM ******
. ****** LPM ******
. ****** LPM ******
. 
. eststo prolpm2: reg supportall2 protest  educ  dem  lengthserve  votemargin blkrep hisprep womanrep   if lib==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    29,882
{txt}{hline 13}{c +}{hline 34}   F(8, 29873)     = {res}    74.59
{txt}       Model {c |} {res} 145.173031         8  18.1466289   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 7268.07297    29,873  .243299065   {txt}R-squared       ={res}    0.0196
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0193
{txt}       Total {c |} {res}   7413.246    29,881  .248092299   {txt}Root MSE        =   {res} .49325

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} supportall2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}protest {c |}{col 14}{res}{space 2} .0468508{col 26}{space 2} .0111096{col 37}{space 1}    4.22{col 46}{space 3}0.000{col 54}{space 4} .0250756{col 67}{space 3} .0686261
{txt}{space 8}educ {c |}{col 14}{res}{space 2} .0659703{col 26}{space 2} .0196316{col 37}{space 1}    3.36{col 46}{space 3}0.001{col 54}{space 4} .0274915{col 67}{space 3} .1044491
{txt}{space 9}dem {c |}{col 14}{res}{space 2} .1306124{col 26}{space 2} .0062127{col 37}{space 1}   21.02{col 46}{space 3}0.000{col 54}{space 4} .1184352{col 67}{space 3} .1427897
{txt}{space 1}lengthserve {c |}{col 14}{res}{space 2}-.0152036{col 26}{space 2} .0182841{col 37}{space 1}   -0.83{col 46}{space 3}0.406{col 54}{space 4}-.0510413{col 67}{space 3} .0206341
{txt}{space 2}votemargin {c |}{col 14}{res}{space 2} .0093911{col 26}{space 2} .0222052{col 37}{space 1}    0.42{col 46}{space 3}0.672{col 54}{space 4} -.034132{col 67}{space 3} .0529142
{txt}{space 6}blkrep {c |}{col 14}{res}{space 2}-.0043445{col 26}{space 2} .0112685{col 37}{space 1}   -0.39{col 46}{space 3}0.700{col 54}{space 4}-.0264313{col 67}{space 3} .0177423
{txt}{space 5}hisprep {c |}{col 14}{res}{space 2} .0422311{col 26}{space 2} .0154839{col 37}{space 1}    2.73{col 46}{space 3}0.006{col 54}{space 4} .0118821{col 67}{space 3} .0725801
{txt}{space 4}womanrep {c |}{col 14}{res}{space 2} .0365673{col 26}{space 2} .0099052{col 37}{space 1}    3.69{col 46}{space 3}0.000{col 54}{space 4} .0171526{col 67}{space 3}  .055982
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3452741{col 26}{space 2} .0173069{col 37}{space 1}   19.95{col 46}{space 3}0.000{col 54}{space 4} .3113518{col 67}{space 3} .3791964
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo plpm2: reg supportall2 i.poorhc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep if lib==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    29,882
{txt}{hline 13}{c +}{hline 34}   F(9, 29872)     = {res}    67.38
{txt}       Model {c |} {res} 147.495266         9  16.3883629   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 7265.75074    29,872   .24322947   {txt}R-squared       ={res}    0.0199
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0196
{txt}       Total {c |} {res}   7413.246    29,881  .248092299   {txt}Root MSE        =   {res} .49318

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                supportall2{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      t{col 61}   P>|t|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}poorhc {c |}
Higher Income Participants  {c |}{col 29}{res}{space 2} .0434923{col 41}{space 2}  .011161{col 52}{space 1}    3.90{col 61}{space 3}0.000{col 69}{space 4} .0216162{col 82}{space 3} .0653684
{txt}{space 3}Low-Income Participants  {c |}{col 29}{res}{space 2} .3440732{col 41}{space 2} .0968307{col 52}{space 1}    3.55{col 61}{space 3}0.000{col 69}{space 4} .1542808{col 82}{space 3} .5338656
{txt}{space 27} {c |}
{space 23}educ {c |}{col 29}{res}{space 2} .0660501{col 41}{space 2} .0196288{col 52}{space 1}    3.36{col 61}{space 3}0.001{col 69}{space 4} .0275768{col 82}{space 3} .1045234
{txt}{space 24}dem {c |}{col 29}{res}{space 2} .1304818{col 41}{space 2}  .006212{col 52}{space 1}   21.00{col 61}{space 3}0.000{col 69}{space 4}  .118306{col 82}{space 3} .1426576
{txt}{space 16}lengthserve {c |}{col 29}{res}{space 2}-.0148626{col 41}{space 2} .0182819{col 52}{space 1}   -0.81{col 61}{space 3}0.416{col 69}{space 4}-.0506958{col 82}{space 3} .0209707
{txt}{space 17}votemargin {c |}{col 29}{res}{space 2} .0088395{col 41}{space 2} .0222027{col 52}{space 1}    0.40{col 61}{space 3}0.691{col 69}{space 4}-.0346788{col 82}{space 3} .0523578
{txt}{space 21}blkrep {c |}{col 29}{res}{space 2}-.0041789{col 41}{space 2}  .011267{col 52}{space 1}   -0.37{col 61}{space 3}0.711{col 69}{space 4}-.0262628{col 82}{space 3} .0179049
{txt}{space 20}hisprep {c |}{col 29}{res}{space 2} .0423196{col 41}{space 2} .0154817{col 52}{space 1}    2.73{col 61}{space 3}0.006{col 69}{space 4} .0119748{col 82}{space 3} .0726643
{txt}{space 19}womanrep {c |}{col 29}{res}{space 2} .0365479{col 41}{space 2} .0099038{col 52}{space 1}    3.69{col 61}{space 3}0.000{col 69}{space 4}  .017136{col 82}{space 3} .0559598
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .3456076{col 41}{space 2} .0173048{col 52}{space 1}   19.97{col 61}{space 3}0.000{col 69}{space 4} .3116895{col 82}{space 3} .3795258
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo nlpm2: reg supportall2 i.nonwhitec  educ  dem  lengthserve  votemargin blkrep hisprep womanrep  if lib==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    29,882
{txt}{hline 13}{c +}{hline 34}   F(9, 29872)     = {res}    68.27
{txt}       Model {c |} {res} 149.417224         9  16.6019138   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 7263.82878    29,872   .24316513   {txt}R-squared       ={res}    0.0202
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0199
{txt}       Total {c |} {res}   7413.246    29,881  .248092299   {txt}Root MSE        =   {res} .49312

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           supportall2{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      t{col 56}   P>|t|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}nonwhitec {c |}
{space 3}White Participants  {c |}{col 24}{res}{space 2} .0251786{col 36}{space 2} .0122582{col 47}{space 1}    2.05{col 56}{space 3}0.040{col 64}{space 4} .0011519{col 77}{space 3} .0492053
{txt}Nonwhite Participants  {c |}{col 24}{res}{space 2} .1318227{col 36}{space 2} .0231738{col 47}{space 1}    5.69{col 56}{space 3}0.000{col 64}{space 4}  .086401{col 77}{space 3} .1772444
{txt}{space 22} {c |}
{space 18}educ {c |}{col 24}{res}{space 2} .0645288{col 36}{space 2} .0196292{col 47}{space 1}    3.29{col 56}{space 3}0.001{col 64}{space 4} .0260546{col 77}{space 3} .1030029
{txt}{space 19}dem {c |}{col 24}{res}{space 2}  .130813{col 36}{space 2} .0062112{col 47}{space 1}   21.06{col 56}{space 3}0.000{col 64}{space 4} .1186387{col 77}{space 3} .1429872
{txt}{space 11}lengthserve {c |}{col 24}{res}{space 2}-.0157631{col 36}{space 2} .0182796{col 47}{space 1}   -0.86{col 56}{space 3}0.389{col 64}{space 4}-.0515919{col 77}{space 3} .0200658
{txt}{space 12}votemargin {c |}{col 24}{res}{space 2} .0117014{col 36}{space 2} .0222059{col 47}{space 1}    0.53{col 56}{space 3}0.598{col 64}{space 4}-.0318232{col 77}{space 3}  .055226
{txt}{space 16}blkrep {c |}{col 24}{res}{space 2}-.0047349{col 36}{space 2} .0112658{col 47}{space 1}   -0.42{col 56}{space 3}0.674{col 64}{space 4}-.0268164{col 77}{space 3} .0173466
{txt}{space 15}hisprep {c |}{col 24}{res}{space 2} .0399854{col 36}{space 2} .0154889{col 47}{space 1}    2.58{col 56}{space 3}0.010{col 64}{space 4} .0096264{col 77}{space 3} .0703444
{txt}{space 14}womanrep {c |}{col 24}{res}{space 2} .0360621{col 36}{space 2} .0099032{col 47}{space 1}    3.64{col 56}{space 3}0.000{col 64}{space 4} .0166513{col 77}{space 3} .0554729
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .3444245{col 36}{space 2} .0173033{col 47}{space 1}   19.91{col 56}{space 3}0.000{col 64}{space 4} .3105092{col 77}{space 3} .3783398
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo niglpm2: reg supportall2 i.noorgigc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep if lib==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    29,866
{txt}{hline 13}{c +}{hline 34}   F(9, 29856)     = {res}    65.97
{txt}       Model {c |} {res} 144.468201         9  16.0520223   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 7264.30833    29,856  .243311506   {txt}R-squared       ={res}    0.0195
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0192
{txt}       Total {c |} {res} 7408.77654    29,865  .248075558   {txt}Root MSE        =   {res} .49327

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 supportall2{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      t{col 62}   P>|t|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}noorgigc {c |}
{space 3}Organized Interest Group  {c |}{col 30}{res}{space 2} .0305939{col 42}{space 2} .0170884{col 53}{space 1}    1.79{col 62}{space 3}0.073{col 70}{space 4}-.0029001{col 83}{space 3} .0640879
{txt}No Organized Interest Group  {c |}{col 30}{res}{space 2} .0541227{col 42}{space 2} .0138386{col 53}{space 1}    3.91{col 62}{space 3}0.000{col 70}{space 4} .0269984{col 83}{space 3}  .081247
{txt}{space 28} {c |}
{space 24}educ {c |}{col 30}{res}{space 2} .0662401{col 42}{space 2} .0196421{col 53}{space 1}    3.37{col 62}{space 3}0.001{col 70}{space 4} .0277407{col 83}{space 3} .1047396
{txt}{space 25}dem {c |}{col 30}{res}{space 2} .1306759{col 42}{space 2} .0062146{col 53}{space 1}   21.03{col 62}{space 3}0.000{col 70}{space 4}  .118495{col 83}{space 3} .1428569
{txt}{space 17}lengthserve {c |}{col 30}{res}{space 2}-.0154514{col 42}{space 2} .0182894{col 53}{space 1}   -0.84{col 62}{space 3}0.398{col 70}{space 4}-.0512994{col 83}{space 3} .0203965
{txt}{space 18}votemargin {c |}{col 30}{res}{space 2}  .010339{col 42}{space 2} .0222164{col 53}{space 1}    0.47{col 62}{space 3}0.642{col 70}{space 4}-.0332061{col 83}{space 3} .0538841
{txt}{space 22}blkrep {c |}{col 30}{res}{space 2}-.0043052{col 42}{space 2} .0112752{col 53}{space 1}   -0.38{col 62}{space 3}0.703{col 70}{space 4} -.026405{col 83}{space 3} .0177946
{txt}{space 21}hisprep {c |}{col 30}{res}{space 2} .0417921{col 42}{space 2} .0154946{col 53}{space 1}    2.70{col 62}{space 3}0.007{col 70}{space 4}  .011422{col 83}{space 3} .0721622
{txt}{space 20}womanrep {c |}{col 30}{res}{space 2} .0363037{col 42}{space 2}  .009911{col 53}{space 1}    3.66{col 62}{space 3}0.000{col 70}{space 4} .0168778{col 83}{space 3} .0557296
{txt}{space 23}_cons {c |}{col 30}{res}{space 2}  .344597{col 42}{space 2} .0173155{col 53}{space 1}   19.90{col 62}{space 3}0.000{col 70}{space 4} .3106579{col 83}{space 3} .3785361
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *** Appendix D, Table 1 ****
. esttab prolpm2 plpm2 nlpm2 niglpm2 using example.tex, booktabs label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars ///
>                  title(Linear Probability Model (Liberal Claims)  \label{c -(}tabreslpm2{c )-}) ///
>                  mtitles("Model I" "Model II" "Model III" "Model IV") ///
>                  order(protest 1.poorhc 2.poorhc 1.nonwhitec 2.nonwhitec 1.noorgigc 2.noorgigc educ  dem  lengthserve  votemargin blkrep hisprep womanrep) ///
>                  addnotes("The dependent variable, legislative support, is binary taking on a value of 0 or 1. All independent variables are coded 0 to 1, except for the protesters' resource variables.") ///
>                  star(* 0.10 ** 0.05 *** 0.01) nobaselevels nodepvars
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
. 
. ****** (LPM with  FE) *******
. ****** (LPM with  FE) *******
. ****** (LPM with  FE) *******
. 
. eststo prolpm2fe: reg supportall2 protest  educ  dem  lengthserve  votemargin blkrep hisprep womanrep   i.statecd1 i.cnrptyy  if lib==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    29,882
{txt}{hline 13}{c +}{hline 34}   F(462, 29419)   = {res}     3.07
{txt}       Model {c |} {res} 340.925583       462  .737934161   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 7072.32042    29,419  .240399756   {txt}R-squared       ={res}    0.0460
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0310
{txt}       Total {c |} {res}   7413.246    29,881  .248092299   {txt}Root MSE        =   {res} .49031

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} supportall2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}protest {c |}{col 14}{res}{space 2} .1052846{col 26}{space 2} .0155437{col 37}{space 1}    6.77{col 46}{space 3}0.000{col 54}{space 4} .0748183{col 67}{space 3} .1357509
{txt}{space 8}educ {c |}{col 14}{res}{space 2}-.0212599{col 26}{space 2} .0506168{col 37}{space 1}   -0.42{col 46}{space 3}0.674{col 54}{space 4}-.1204711{col 67}{space 3} .0779513
{txt}{space 9}dem {c |}{col 14}{res}{space 2} .1020195{col 26}{space 2} .0129996{col 37}{space 1}    7.85{col 46}{space 3}0.000{col 54}{space 4} .0765397{col 67}{space 3} .1274992
{txt}{space 1}lengthserve {c |}{col 14}{res}{space 2}-.0426224{col 26}{space 2} .0333284{col 37}{space 1}   -1.28{col 46}{space 3}0.201{col 54}{space 4}-.1079476{col 67}{space 3} .0227028
{txt}{space 2}votemargin {c |}{col 14}{res}{space 2} .2688906{col 26}{space 2} .0326603{col 37}{space 1}    8.23{col 46}{space 3}0.000{col 54}{space 4} .2048749{col 67}{space 3} .3329063
{txt}{space 6}blkrep {c |}{col 14}{res}{space 2}-.0922643{col 26}{space 2} .0263487{col 37}{space 1}   -3.50{col 46}{space 3}0.000{col 54}{space 4} -.143909{col 67}{space 3}-.0406196
{txt}{space 5}hisprep {c |}{col 14}{res}{space 2}-.0305394{col 26}{space 2} .0423442{col 37}{space 1}   -0.72{col 46}{space 3}0.471{col 54}{space 4} -.113536{col 67}{space 3} .0524572
{txt}{space 4}womanrep {c |}{col 14}{res}{space 2} .0399448{col 26}{space 2} .0206711{col 37}{space 1}    1.93{col 46}{space 3}0.053{col 54}{space 4}-.0005714{col 67}{space 3} .0804611
{txt}{space 12} {c |}
{space 4}statecd1 {c |}
{space 8}1 2  {c |}{col 14}{res}{space 2}-.0519542{col 26}{space 2} .0862561{col 37}{space 1}   -0.60{col 46}{space 3}0.547{col 54}{space 4}-.2210199{col 67}{space 3} .1171116
{txt}{space 8}1 3  {c |}{col 14}{res}{space 2} .0264343{col 26}{space 2} .0872863{col 37}{space 1}    0.30{col 46}{space 3}0.762{col 54}{space 4}-.1446508{col 67}{space 3} .1975194
{txt}{space 8}1 4  {c |}{col 14}{res}{space 2} .0298382{col 26}{space 2} .0884753{col 37}{space 1}    0.34{col 46}{space 3}0.736{col 54}{space 4}-.1435772{col 67}{space 3} .2032537
{txt}{space 8}1 5  {c |}{col 14}{res}{space 2} .0332603{col 26}{space 2} .0878196{col 37}{space 1}    0.38{col 46}{space 3}0.705{col 54}{space 4}  -.13887{col 67}{space 3} .2053906
{txt}{space 8}1 6  {c |}{col 14}{res}{space 2}-.0556609{col 26}{space 2} .0865377{col 37}{space 1}   -0.64{col 46}{space 3}0.520{col 54}{space 4}-.2252787{col 67}{space 3} .1139569
{txt}{space 8}1 7  {c |}{col 14}{res}{space 2} .1189449{col 26}{space 2} .0889897{col 37}{space 1}    1.34{col 46}{space 3}0.181{col 54}{space 4}-.0554788{col 67}{space 3} .2933686
{txt}{space 7}10 1  {c |}{col 14}{res}{space 2} .1166591{col 26}{space 2} .0859653{col 37}{space 1}    1.36{col 46}{space 3}0.175{col 54}{space 4}-.0518368{col 67}{space 3} .2851549
{txt}{space 7}12 1  {c |}{col 14}{res}{space 2}-.0062115{col 26}{space 2} .0877417{col 37}{space 1}   -0.07{col 46}{space 3}0.944{col 54}{space 4}-.1781891{col 67}{space 3} .1657661
{txt}{space 6}12 10  {c |}{col 14}{res}{space 2} .0502878{col 26}{space 2} .0874799{col 37}{space 1}    0.57{col 46}{space 3}0.565{col 54}{space 4}-.1211768{col 67}{space 3} .2217524
{txt}{space 6}12 11  {c |}{col 14}{res}{space 2} .0925346{col 26}{space 2} .0895255{col 37}{space 1}    1.03{col 46}{space 3}0.301{col 54}{space 4}-.0829394{col 67}{space 3} .2680086
{txt}{space 6}12 12  {c |}{col 14}{res}{space 2} .0825396{col 26}{space 2} .0863584{col 37}{space 1}    0.96{col 46}{space 3}0.339{col 54}{space 4}-.0867267{col 67}{space 3}  .251806
{txt}{space 6}12 13  {c |}{col 14}{res}{space 2}-.0910377{col 26}{space 2}  .086082{col 37}{space 1}   -1.06{col 46}{space 3}0.290{col 54}{space 4}-.2597623{col 67}{space 3} .0776869
{txt}{space 6}12 14  {c |}{col 14}{res}{space 2}-.0429603{col 26}{space 2} .0863905{col 37}{space 1}   -0.50{col 46}{space 3}0.619{col 54}{space 4}-.2122895{col 67}{space 3} .1263689
{txt}{space 6}12 15  {c |}{col 14}{res}{space 2} .1883637{col 26}{space 2} .0868548{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0181244{col 67}{space 3} .3586031
{txt}{space 6}12 16  {c |}{col 14}{res}{space 2} .1202817{col 26}{space 2} .0885923{col 37}{space 1}    1.36{col 46}{space 3}0.175{col 54}{space 4}-.0533632{col 67}{space 3} .2939266
{txt}{space 6}12 17  {c |}{col 14}{res}{space 2} .1431731{col 26}{space 2} .0913067{col 37}{space 1}    1.57{col 46}{space 3}0.117{col 54}{space 4}-.0357922{col 67}{space 3} .3221384
{txt}{space 6}12 18  {c |}{col 14}{res}{space 2} .1807344{col 26}{space 2} .0990012{col 37}{space 1}    1.83{col 46}{space 3}0.068{col 54}{space 4}-.0133124{col 67}{space 3} .3747813
{txt}{space 6}12 19  {c |}{col 14}{res}{space 2} .0650762{col 26}{space 2} .0878486{col 37}{space 1}    0.74{col 46}{space 3}0.459{col 54}{space 4}-.1071109{col 67}{space 3} .2372634
{txt}{space 7}12 2  {c |}{col 14}{res}{space 2} .0580212{col 26}{space 2} .0874526{col 37}{space 1}    0.66{col 46}{space 3}0.507{col 54}{space 4}-.1133898{col 67}{space 3} .2294322
{txt}{space 6}12 20  {c |}{col 14}{res}{space 2} .0560219{col 26}{space 2} .0942743{col 37}{space 1}    0.59{col 46}{space 3}0.552{col 54}{space 4}  -.12876{col 67}{space 3} .2408037
{txt}{space 6}12 21  {c |}{col 14}{res}{space 2} .1775629{col 26}{space 2} .1007632{col 37}{space 1}    1.76{col 46}{space 3}0.078{col 54}{space 4}-.0199376{col 67}{space 3} .3750634
{txt}{space 6}12 22  {c |}{col 14}{res}{space 2} .0330179{col 26}{space 2} .0915015{col 37}{space 1}    0.36{col 46}{space 3}0.718{col 54}{space 4}-.1463292{col 67}{space 3} .2123649
{txt}{space 6}12 23  {c |}{col 14}{res}{space 2} .0395916{col 26}{space 2} .0977239{col 37}{space 1}    0.41{col 46}{space 3}0.685{col 54}{space 4}-.1519516{col 67}{space 3} .2311347
{txt}{space 7}12 3  {c |}{col 14}{res}{space 2} .0873937{col 26}{space 2} .0895091{col 37}{space 1}    0.98{col 46}{space 3}0.329{col 54}{space 4}-.0880482{col 67}{space 3} .2628356
{txt}{space 7}12 4  {c |}{col 14}{res}{space 2}-.0127498{col 26}{space 2} .0881426{col 37}{space 1}   -0.14{col 46}{space 3}0.885{col 54}{space 4}-.1855133{col 67}{space 3} .1600137
{txt}{space 7}12 5  {c |}{col 14}{res}{space 2}-.0105904{col 26}{space 2} .0875427{col 37}{space 1}   -0.12{col 46}{space 3}0.904{col 54}{space 4}-.1821781{col 67}{space 3} .1609973
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{txt}{space 6}13 11  {c |}{col 14}{res}{space 2} .1389888{col 26}{space 2} .1005964{col 37}{space 1}    1.38{col 46}{space 3}0.167{col 54}{space 4}-.0581847{col 67}{space 3} .3361623
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{txt}{space 12} {c |}
{space 5}cnrptyy {c |}
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{space 7}_cons {c |}{col 14}{res}{space 2} .1297822{col 26}{space 2}  .068133{col 37}{space 1}    1.90{col 46}{space 3}0.057{col 54}{space 4}-.0037616{col 67}{space 3}  .263326
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo plpm2fe: reg supportall2 i.poorhc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep   i.statecd1 i.cnrptyy  if lib==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    29,882
{txt}{hline 13}{c +}{hline 34}   F(463, 29418)   = {res}     3.09
{txt}       Model {c |} {res} 343.774603       463  .742493743   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  7069.4714    29,418  .240311082   {txt}R-squared       ={res}    0.0464
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0314
{txt}       Total {c |} {res}   7413.246    29,881  .248092299   {txt}Root MSE        =   {res} .49022

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                supportall2{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      t{col 61}   P>|t|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}poorhc {c |}
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{txt}{space 27} {c |}
{space 19}statecd1 {c |}
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{space 22}_cons {c |}{col 29}{res}{space 2} .1293927{col 41}{space 2} .0681206{col 52}{space 1}    1.90{col 61}{space 3}0.058{col 69}{space 4}-.0041266{col 82}{space 3} .2629121
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo nlpm2fe: reg supportall2 i.nonwhitec  educ  dem  lengthserve  votemargin blkrep hisprep womanrep   i.statecd1 i.cnrptyy  if lib==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    29,882
{txt}{hline 13}{c +}{hline 34}   F(463, 29418)   = {res}     3.09
{txt}       Model {c |} {res} 343.486469       463  .741871423   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 7069.75953    29,418  .240320876   {txt}R-squared       ={res}    0.0463
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0313
{txt}       Total {c |} {res}   7413.246    29,881  .248092299   {txt}Root MSE        =   {res} .49023

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           supportall2{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      t{col 56}   P>|t|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}nonwhitec {c |}
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{txt}{space 22} {c |}
{space 15}cnrptyy {c |}
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{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2} .1323942{col 36}{space 2} .0681266{col 47}{space 1}    1.94{col 56}{space 3}0.052{col 64}{space 4}-.0011369{col 77}{space 3} .2659253
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. eststo niglpm2fe: reg supportall2 i.noorgigc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep   i.statecd1 i.cnrptyy  if lib==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    29,866
{txt}{hline 13}{c +}{hline 34}   F(463, 29402)   = {res}     3.06
{txt}       Model {c |} {res} 340.282543       463  .734951497   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 7068.49399    29,402  .240408611   {txt}R-squared       ={res}    0.0459
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0309
{txt}       Total {c |} {res} 7408.77654    29,865  .248075558   {txt}Root MSE        =   {res} .49031

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 supportall2{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      t{col 62}   P>|t|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}noorgigc {c |}
{space 3}Organized Interest Group  {c |}{col 30}{res}{space 2} .0956282{col 42}{space 2} .0208487{col 53}{space 1}    4.59{col 62}{space 3}0.000{col 70}{space 4} .0547639{col 83}{space 3} .1364926
{txt}No Organized Interest Group  {c |}{col 30}{res}{space 2} .1093749{col 42}{space 2} .0172416{col 53}{space 1}    6.34{col 62}{space 3}0.000{col 70}{space 4} .0755806{col 83}{space 3} .1431692
{txt}{space 28} {c |}
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{txt}{space 25}dem {c |}{col 30}{res}{space 2} .1024382{col 42}{space 2} .0130035{col 53}{space 1}    7.88{col 62}{space 3}0.000{col 70}{space 4} .0769508{col 83}{space 3} .1279256
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{txt}{space 22}blkrep {c |}{col 30}{res}{space 2} -.091957{col 42}{space 2} .0263627{col 53}{space 1}   -3.49{col 62}{space 3}0.000{col 70}{space 4}-.1436291{col 83}{space 3}-.0402849
{txt}{space 21}hisprep {c |}{col 30}{res}{space 2}-.0320024{col 42}{space 2} .0423852{col 53}{space 1}   -0.76{col 62}{space 3}0.450{col 70}{space 4}-.1150792{col 83}{space 3} .0510744
{txt}{space 20}womanrep {c |}{col 30}{res}{space 2} .0393442{col 42}{space 2} .0206778{col 53}{space 1}    1.90{col 62}{space 3}0.057{col 70}{space 4}-.0011851{col 83}{space 3} .0798735
{txt}{space 28} {c |}
{space 20}statecd1 {c |}
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{txt}{space 28} {c |}
{space 21}cnrptyy {c |}
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{txt}{space 28} {c |}
{space 23}_cons {c |}{col 30}{res}{space 2} .1301931{col 42}{space 2} .0681388{col 53}{space 1}    1.91{col 62}{space 3}0.056{col 70}{space 4}-.0033621{col 83}{space 3} .2637482
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *** Appendix D, Table 2 ****
. esttab prolpm2fe plpm2fe nlpm2fe niglpm2fe using example.tex, booktabs label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars ///
>                  title(Linear Probability Model with Fixed Effects (Liberal Claims)  \label{c -(}tabreslpmfe2{c )-}) ///
>                  mtitles("Model I" "Model II" "Model III" "Model IV" ) ///
>                  indicate("Congressional District Dummies = *statecd1" "Congress Dummies = *cnrptyy") ///
>                  order(protest 1.poorhc 2.poorhc 1.nonwhitec 2.nonwhitec 1.noorgigc 2.noorgigc educ  dem  lengthserve  votemargin blkrep hisprep womanrep ) ///
>                  addnotes("The dependent variable, legislative support, is binary taking on a value of 0 or 1. All independent variables are coded 0 to 1, except for the protesters' resource variables.") ///
>                  star(* 0.10 ** 0.05 *** 0.01) nobaselevels nodepvars
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
. 
. 
. ****** (Logistic regressions with FE) *******
. ****** (Logistic regressions with FE) *******
. ****** (Logistic regressions with FE) *******
. 
. eststo profe2lib: logit supportall2 protest  educ  dem  lengthserve  votemargin blkrep hisprep womanrep  i.statecd1 i.cnrptyy  if lib==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-20597.969}  
Iteration 1:{space 3}log likelihood = {res:-19893.154}  
Iteration 2:{space 3}log likelihood = {res: -19889.08}  
Iteration 3:{space 3}log likelihood = {res:-19889.064}  
Iteration 4:{space 3}log likelihood = {res:-19889.064}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,882
{txt}{col 49}LR chi2({res}462{txt}){col 67}= {res}   1417.81
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-19889.064{txt}{col 49}Pseudo R2{col 67}= {res}    0.0344

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} supportall2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
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{txt}{space 12} {c |}
{space 4}statecd1 {c |}
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{txt}{space 12} {c |}
{space 5}cnrptyy {c |}
{space 8}103  {c |}{col 14}{res}{space 2} .1873894{col 26}{space 2} .0360692{col 37}{space 1}    5.20{col 46}{space 3}0.000{col 54}{space 4}  .116695{col 67}{space 3} .2580837
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-1.560924{col 26}{space 2} .2933596{col 37}{space 1}   -5.32{col 46}{space 3}0.000{col 54}{space 4}-2.135898{col 67}{space 3}-.9859494
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. eststo pfe2lib: logit supportall2 i.poorhc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep   i.statecd1 i.cnrptyy  if lib==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-20597.969}  
Iteration 1:{space 3}log likelihood = {res:-19886.915}  
Iteration 2:{space 3}log likelihood = {res:-19882.632}  
Iteration 3:{space 3}log likelihood = {res:-19882.614}  
Iteration 4:{space 3}log likelihood = {res:-19882.614}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,882
{txt}{col 49}LR chi2({res}463{txt}){col 67}= {res}   1430.71
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-19882.614{txt}{col 49}Pseudo R2{col 67}= {res}    0.0347

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                supportall2{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}poorhc {c |}
Higher Income Participants  {c |}{col 29}{res}{space 2} .4299685{col 41}{space 2} .0649862{col 52}{space 1}    6.62{col 61}{space 3}0.000{col 69}{space 4} .3025979{col 82}{space 3}  .557339
{txt}{space 3}Low-Income Participants  {c |}{col 29}{res}{space 2} 2.112822{col 41}{space 2} .5510627{col 52}{space 1}    3.83{col 61}{space 3}0.000{col 69}{space 4} 1.032759{col 82}{space 3} 3.192885
{txt}{space 27} {c |}
{space 23}educ {c |}{col 29}{res}{space 2}-.0927094{col 41}{space 2} .2119276{col 52}{space 1}   -0.44{col 61}{space 3}0.662{col 69}{space 4}-.5080798{col 82}{space 3}  .322661
{txt}{space 24}dem {c |}{col 29}{res}{space 2} .4212327{col 41}{space 2} .0541976{col 52}{space 1}    7.77{col 61}{space 3}0.000{col 69}{space 4} .3150074{col 82}{space 3} .5274581
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{txt}{space 17}votemargin {c |}{col 29}{res}{space 2} 1.143948{col 41}{space 2} .1381482{col 52}{space 1}    8.28{col 61}{space 3}0.000{col 69}{space 4} .8731826{col 82}{space 3} 1.414713
{txt}{space 21}blkrep {c |}{col 29}{res}{space 2}-.3876364{col 41}{space 2} .1098481{col 52}{space 1}   -3.53{col 61}{space 3}0.000{col 69}{space 4}-.6029348{col 82}{space 3}-.1723381
{txt}{space 20}hisprep {c |}{col 29}{res}{space 2}-.1349252{col 41}{space 2} .1761051{col 52}{space 1}   -0.77{col 61}{space 3}0.444{col 69}{space 4}-.4800849{col 82}{space 3} .2102345
{txt}{space 19}womanrep {c |}{col 29}{res}{space 2} .1696631{col 41}{space 2} .0864918{col 52}{space 1}    1.96{col 61}{space 3}0.050{col 69}{space 4} .0001422{col 82}{space 3}  .339184
{txt}{space 27} {c |}
{space 19}statecd1 {c |}
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{txt}{space 27} {c |}
{space 20}cnrptyy {c |}
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{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2}-1.563209{col 41}{space 2} .2933807{col 52}{space 1}   -5.33{col 61}{space 3}0.000{col 69}{space 4}-2.138225{col 82}{space 3}-.9881938
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. eststo nfe2lib: logit supportall2 i.nonwhitec  educ  dem  lengthserve  votemargin blkrep hisprep womanrep  i.statecd1 i.cnrptyy  if lib==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-20597.969}  
Iteration 1:{space 3}log likelihood = {res:-19887.863}  
Iteration 2:{space 3}log likelihood = {res:-19883.779}  
Iteration 3:{space 3}log likelihood = {res:-19883.762}  
Iteration 4:{space 3}log likelihood = {res:-19883.762}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,882
{txt}{col 49}LR chi2({res}463{txt}){col 67}= {res}   1428.41
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-19883.762{txt}{col 49}Pseudo R2{col 67}= {res}    0.0347

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           supportall2{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}nonwhitec {c |}
{space 3}White Participants  {c |}{col 24}{res}{space 2} .3485604{col 36}{space 2} .0703473{col 47}{space 1}    4.95{col 56}{space 3}0.000{col 64}{space 4} .2106821{col 77}{space 3} .4864386
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{txt}{space 22} {c |}
{space 18}educ {c |}{col 24}{res}{space 2}-.1165499{col 36}{space 2} .2118665{col 47}{space 1}   -0.55{col 56}{space 3}0.582{col 64}{space 4}-.5318005{col 77}{space 3} .2987007
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{txt}{space 22} {c |}
{space 14}statecd1 {c |}
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{txt}{space 22} {c |}
{space 15}cnrptyy {c |}
{space 18}103  {c |}{col 24}{res}{space 2} .1865653{col 36}{space 2} .0360745{col 47}{space 1}    5.17{col 56}{space 3}0.000{col 64}{space 4} .1158605{col 77}{space 3} .2572701
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{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2}-1.549602{col 36}{space 2}  .293398{col 47}{space 1}   -5.28{col 56}{space 3}0.000{col 64}{space 4}-2.124652{col 77}{space 3}-.9745526
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. eststo nigfe2lib: logit supportall2 i.noorgigc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep  i.statecd1 i.cnrptyy  if lib==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-20585.938}  
Iteration 1:{space 3}log likelihood = {res:-19882.395}  
Iteration 2:{space 3}log likelihood = {res:-19878.322}  
Iteration 3:{space 3}log likelihood = {res:-19878.305}  
Iteration 4:{space 3}log likelihood = {res:-19878.305}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,866
{txt}{col 49}LR chi2({res}463{txt}){col 67}= {res}   1415.26
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-19878.305{txt}{col 49}Pseudo R2{col 67}= {res}    0.0344

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 supportall2{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}noorgigc {c |}
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{txt}No Organized Interest Group  {c |}{col 30}{res}{space 2} .4550284{col 42}{space 2} .0720587{col 53}{space 1}    6.31{col 62}{space 3}0.000{col 70}{space 4} .3137961{col 83}{space 3} .5962608
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{txt}{space 28} {c |}
{space 20}statecd1 {c |}
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{txt}{space 28} {c |}
{space 21}cnrptyy {c |}
{space 24}103  {c |}{col 30}{res}{space 2} .1867441{col 42}{space 2} .0360917{col 53}{space 1}    5.17{col 62}{space 3}0.000{col 70}{space 4} .1160056{col 83}{space 3} .2574826
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{txt}{space 28} {c |}
{space 23}_cons {c |}{col 30}{res}{space 2}-1.559446{col 42}{space 2} .2933886{col 53}{space 1}   -5.32{col 62}{space 3}0.000{col 70}{space 4}-2.134477{col 83}{space 3}-.9844149
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *** Appendix D, Table 3 ****
. esttab profe2lib pfe2lib nfe2lib nigfe2lib using example.tex, booktabs scalars("ll Log likelihood") label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars ///
>                  title(Logistic Regressions with Fixed Effects (Liberal Claims) \label{c -(}tabresfe2{c )-}) ///
>                  mtitles("Model I" "Model II" "Model III" "Model IV") ///
>                  indicate("Congressional District Dummies = *statecd1" "Congress Dummies = *cnrptyy") ///
>                  order(protest 1.poorhc 2.poorhc 1.nonwhitec 2.nonwhitec 1.noorgigc 2.noorgigc educ  dem  lengthserve  votemargin blkrep hisprep womanrep ) ///
>                  addnotes("Coefficients are Log Odds. The dependent variable, legislative support, is binary taking on a value of 0 or 1. All independent variables are coded 0 to 1.") ///
>                  star(* 0.10 ** 0.05 *** 0.01) nobaselevels
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
. 
. ****** Logit with RE, conservative claims *******
. ****** Logit with RE, conservative claims *******
. ****** Logit with RE, conservative claims *******
. 
. eststo prore2cons, title("Model I"):  melogit supportall2 protest  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if cons==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    21,578
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       437

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      49.4
{col 63}{txt}max{col 67}={res}{col 69}        73

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={res}{col 70}   470.66
{txt}Log likelihood = {res}-14419.984{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} supportall2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}protest {c |}{col 14}{res}{space 2} .6934618{col 26}{space 2} .0830103{col 37}{space 1}    8.35{col 46}{space 3}0.000{col 54}{space 4} .5307645{col 67}{space 3}  .856159
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{txt}{space 9}dem {c |}{col 14}{res}{space 2} .5987652{col 26}{space 2} .0349574{col 37}{space 1}   17.13{col 46}{space 3}0.000{col 54}{space 4} .5302501{col 67}{space 3} .6672804
{txt}{space 1}lengthserve {c |}{col 14}{res}{space 2}-.0753602{col 26}{space 2} .1080147{col 37}{space 1}   -0.70{col 46}{space 3}0.485{col 54}{space 4}-.2870652{col 67}{space 3} .1363447
{txt}{space 2}votemargin {c |}{col 14}{res}{space 2} .3725484{col 26}{space 2} .1244935{col 37}{space 1}    2.99{col 46}{space 3}0.003{col 54}{space 4} .1285456{col 67}{space 3} .6165511
{txt}{space 6}blkrep {c |}{col 14}{res}{space 2}-.0477668{col 26}{space 2} .1248748{col 37}{space 1}   -0.38{col 46}{space 3}0.702{col 54}{space 4} -.292517{col 67}{space 3} .1969833
{txt}{space 5}hisprep {c |}{col 14}{res}{space 2}-.0496194{col 26}{space 2} .1585041{col 37}{space 1}   -0.31{col 46}{space 3}0.754{col 54}{space 4}-.3602816{col 67}{space 3} .2610429
{txt}{space 4}womanrep {c |}{col 14}{res}{space 2} .2030381{col 26}{space 2} .0627698{col 37}{space 1}    3.23{col 46}{space 3}0.001{col 54}{space 4} .0800116{col 67}{space 3} .3260646
{txt}{space 12} {c |}
{space 5}cnrptyy {c |}
{space 8}103  {c |}{col 14}{res}{space 2}-.0117676{col 26}{space 2} .0353129{col 37}{space 1}   -0.33{col 46}{space 3}0.739{col 54}{space 4}-.0809797{col 67}{space 3} .0574444
{txt}{space 8}104  {c |}{col 14}{res}{space 2} .0112279{col 26}{space 2} .0385199{col 37}{space 1}    0.29{col 46}{space 3}0.771{col 54}{space 4}-.0642698{col 67}{space 3} .0867256
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.7556897{col 26}{space 2} .1031588{col 37}{space 1}   -7.33{col 46}{space 3}0.000{col 54}{space 4}-.9578772{col 67}{space 3}-.5535022
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .0427126{col 26}{space 2} .0097111{col 54}{space 4} .0273544{col 67}{space 3} .0666937
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 36.38{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. eststo pre2cons, title("Model II"):  melogit supportall2 i.poorhc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if  cons==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    21,578
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       437

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      49.4
{col 63}{txt}max{col 67}={res}{col 69}        73

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={res}{col 70}   470.66
{txt}Log likelihood = {res}-14419.984{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 supportall2{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 28} {c |}
{space 22}poorhc {c |}
{space 1}Higher Income Participants  {c |}{col 30}{res}{space 2} .6934618{col 42}{space 2} .0830103{col 53}{space 1}    8.35{col 62}{space 3}0.000{col 70}{space 4} .5307645{col 83}{space 3}  .856159
{txt}{space 24}educ {c |}{col 30}{res}{space 2}-.2817542{col 42}{space 2} .1405918{col 53}{space 1}   -2.00{col 62}{space 3}0.045{col 70}{space 4} -.557309{col 83}{space 3}-.0061995
{txt}{space 25}dem {c |}{col 30}{res}{space 2} .5987652{col 42}{space 2} .0349574{col 53}{space 1}   17.13{col 62}{space 3}0.000{col 70}{space 4} .5302501{col 83}{space 3} .6672804
{txt}{space 17}lengthserve {c |}{col 30}{res}{space 2}-.0753602{col 42}{space 2} .1080147{col 53}{space 1}   -0.70{col 62}{space 3}0.485{col 70}{space 4}-.2870652{col 83}{space 3} .1363447
{txt}{space 18}votemargin {c |}{col 30}{res}{space 2} .3725484{col 42}{space 2} .1244935{col 53}{space 1}    2.99{col 62}{space 3}0.003{col 70}{space 4} .1285456{col 83}{space 3} .6165511
{txt}{space 22}blkrep {c |}{col 30}{res}{space 2}-.0477668{col 42}{space 2} .1248748{col 53}{space 1}   -0.38{col 62}{space 3}0.702{col 70}{space 4} -.292517{col 83}{space 3} .1969833
{txt}{space 21}hisprep {c |}{col 30}{res}{space 2}-.0496194{col 42}{space 2} .1585041{col 53}{space 1}   -0.31{col 62}{space 3}0.754{col 70}{space 4}-.3602816{col 83}{space 3} .2610429
{txt}{space 20}womanrep {c |}{col 30}{res}{space 2} .2030381{col 42}{space 2} .0627698{col 53}{space 1}    3.23{col 62}{space 3}0.001{col 70}{space 4} .0800116{col 83}{space 3} .3260646
{txt}{space 28} {c |}
{space 21}cnrptyy {c |}
{space 24}103  {c |}{col 30}{res}{space 2}-.0117676{col 42}{space 2} .0353129{col 53}{space 1}   -0.33{col 62}{space 3}0.739{col 70}{space 4}-.0809797{col 83}{space 3} .0574444
{txt}{space 24}104  {c |}{col 30}{res}{space 2} .0112279{col 42}{space 2} .0385199{col 53}{space 1}    0.29{col 62}{space 3}0.771{col 70}{space 4}-.0642698{col 83}{space 3} .0867256
{txt}{space 28} {c |}
{space 23}_cons {c |}{col 30}{res}{space 2}-.7556897{col 42}{space 2} .1031588{col 53}{space 1}   -7.33{col 62}{space 3}0.000{col 70}{space 4}-.9578772{col 83}{space 3}-.5535022
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                     {col 30}{c |}
                   var(_cons){c |}{col 30}{res}{space 2} .0427126{col 42}{space 2} .0097111{col 70}{space 4} .0273544{col 83}{space 3} .0666937
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 36.38{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. eststo nre2cons, title("Model III"):  melogit supportall2 i.nonwhitec educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if  cons==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    21,578
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       437

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      49.4
{col 63}{txt}max{col 67}={res}{col 69}        73

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   470.71
{txt}Log likelihood = {res}-14419.964{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            supportall2{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23} {c |}
{space 14}nonwhitec {c |}
{space 4}White Participants  {c |}{col 25}{res}{space 2} .6887271{col 37}{space 2} .0862527{col 48}{space 1}    7.98{col 57}{space 3}0.000{col 65}{space 4}  .519675{col 78}{space 3} .8577792
{txt}{space 1}Nonwhite Participants  {c |}{col 25}{res}{space 2} .7367988{col 37}{space 2} .2312259{col 48}{space 1}    3.19{col 57}{space 3}0.001{col 65}{space 4} .2836044{col 78}{space 3} 1.189993
{txt}{space 23} {c |}
{space 19}educ {c |}{col 25}{res}{space 2}-.2821172{col 37}{space 2} .1406018{col 48}{space 1}   -2.01{col 57}{space 3}0.045{col 65}{space 4}-.5576916{col 78}{space 3}-.0065428
{txt}{space 20}dem {c |}{col 25}{res}{space 2} .5988021{col 37}{space 2} .0349569{col 48}{space 1}   17.13{col 57}{space 3}0.000{col 65}{space 4} .5302878{col 78}{space 3} .6673163
{txt}{space 12}lengthserve {c |}{col 25}{res}{space 2}-.0753547{col 37}{space 2} .1080115{col 48}{space 1}   -0.70{col 57}{space 3}0.485{col 65}{space 4}-.2870533{col 78}{space 3} .1363439
{txt}{space 13}votemargin {c |}{col 25}{res}{space 2} .3723361{col 37}{space 2} .1244938{col 48}{space 1}    2.99{col 57}{space 3}0.003{col 65}{space 4} .1283326{col 78}{space 3} .6163395
{txt}{space 17}blkrep {c |}{col 25}{res}{space 2}-.0485311{col 37}{space 2} .1249408{col 48}{space 1}   -0.39{col 57}{space 3}0.698{col 65}{space 4}-.2934105{col 78}{space 3} .1963484
{txt}{space 16}hisprep {c |}{col 25}{res}{space 2}-.0516747{col 37}{space 2} .1588387{col 48}{space 1}   -0.33{col 57}{space 3}0.745{col 65}{space 4}-.3629928{col 78}{space 3} .2596434
{txt}{space 15}womanrep {c |}{col 25}{res}{space 2} .2027832{col 37}{space 2} .0627827{col 48}{space 1}    3.23{col 57}{space 3}0.001{col 65}{space 4} .0797314{col 78}{space 3}  .325835
{txt}{space 23} {c |}
{space 16}cnrptyy {c |}
{space 19}103  {c |}{col 25}{res}{space 2}-.0117633{col 37}{space 2} .0353123{col 48}{space 1}   -0.33{col 57}{space 3}0.739{col 65}{space 4} -.080974{col 78}{space 3} .0574475
{txt}{space 19}104  {c |}{col 25}{res}{space 2} .0108679{col 37}{space 2} .0385618{col 48}{space 1}    0.28{col 57}{space 3}0.778{col 65}{space 4}-.0647119{col 78}{space 3} .0864476
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} -.755312{col 37}{space 2} .1031705{col 48}{space 1}   -7.32{col 57}{space 3}0.000{col 65}{space 4}-.9575225{col 78}{space 3}-.5531015
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                {col 25}{c |}
              var(_cons){c |}{col 25}{res}{space 2} .0427013{col 37}{space 2}  .009711{col 65}{space 4} .0273441{col 78}{space 3} .0666836
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 36.36{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. eststo nigre2cons, title("Model IV"):  melogit supportall2 i.noorgigc  educ  dem  lengthserve  votemargin blkrep hisprep womanrep i.cnrptyy if  cons==1|| statecd:, nolog 
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    21,578
{txt}Group variable: {col 25}{res}statecd{col 49}{txt}Number of groups{col 67}={res}{col 69}       437

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      49.4
{col 63}{txt}max{col 67}={res}{col 69}        73

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   476.30
{txt}Log likelihood = {res}-14413.847{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                  supportall2{col 31}{c |}      Coef.{col 43}   Std. Err.{col 55}      z{col 63}   P>|z|{col 71}     [95% Con{col 84}f. Interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29} {c |}
{space 21}noorgigc {c |}
{space 4}Organized Interest Group  {c |}{col 31}{res}{space 2} .4125659{col 43}{space 2} .1145121{col 54}{space 1}    3.60{col 63}{space 3}0.000{col 71}{space 4} .1881263{col 84}{space 3} .6370056
{txt}{space 1}No Organized Interest Group  {c |}{col 31}{res}{space 2} .9262521{col 43}{space 2} .1081551{col 54}{space 1}    8.56{col 63}{space 3}0.000{col 71}{space 4}  .714272{col 84}{space 3} 1.138232
{txt}{space 29} {c |}
{space 25}educ {c |}{col 31}{res}{space 2} -.280109{col 43}{space 2} .1412106{col 54}{space 1}   -1.98{col 63}{space 3}0.047{col 71}{space 4}-.5568767{col 84}{space 3}-.0033412
{txt}{space 26}dem {c |}{col 31}{res}{space 2} .6001374{col 43}{space 2} .0350933{col 54}{space 1}   17.10{col 63}{space 3}0.000{col 71}{space 4} .5313558{col 84}{space 3}  .668919
{txt}{space 18}lengthserve {c |}{col 31}{res}{space 2}-.0798674{col 43}{space 2} .1083764{col 54}{space 1}   -0.74{col 63}{space 3}0.461{col 71}{space 4}-.2922812{col 84}{space 3} .1325465
{txt}{space 19}votemargin {c |}{col 31}{res}{space 2} .3837193{col 43}{space 2} .1248585{col 54}{space 1}    3.07{col 63}{space 3}0.002{col 71}{space 4}  .139001{col 84}{space 3} .6284375
{txt}{space 23}blkrep {c |}{col 31}{res}{space 2}-.0459973{col 43}{space 2} .1255634{col 54}{space 1}   -0.37{col 63}{space 3}0.714{col 71}{space 4} -.292097{col 84}{space 3} .2001024
{txt}{space 22}hisprep {c |}{col 31}{res}{space 2}-.0584183{col 43}{space 2} .1594104{col 54}{space 1}   -0.37{col 63}{space 3}0.714{col 71}{space 4}-.3708569{col 84}{space 3} .2540203
{txt}{space 21}womanrep {c |}{col 31}{res}{space 2} .2027095{col 43}{space 2} .0630291{col 54}{space 1}    3.22{col 63}{space 3}0.001{col 71}{space 4} .0791748{col 84}{space 3} .3262442
{txt}{space 29} {c |}
{space 22}cnrptyy {c |}
{space 25}103  {c |}{col 31}{res}{space 2}-.0083215{col 43}{space 2} .0353686{col 54}{space 1}   -0.24{col 63}{space 3}0.814{col 71}{space 4}-.0776427{col 84}{space 3} .0609997
{txt}{space 25}104  {c |}{col 31}{res}{space 2} .0130679{col 43}{space 2} .0385637{col 54}{space 1}    0.34{col 63}{space 3}0.735{col 71}{space 4}-.0625156{col 84}{space 3} .0886513
{txt}{space 29} {c |}
{space 24}_cons {c |}{col 31}{res}{space 2}-.7653082{col 43}{space 2} .1035248{col 54}{space 1}   -7.39{col 63}{space 3}0.000{col 71}{space 4}-.9682132{col 84}{space 3}-.5624032
{txt}{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}statecd                      {col 31}{c |}
                    var(_cons){c |}{col 31}{res}{space 2}  .043938{col 43}{space 2} .0098688{col 71}{space 4} .0282914{col 84}{space 3} .0682381
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 37.75{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}. 
. *** Appendix D, Table 4 ****
. esttab prore2cons pre2cons nre2cons nigre2cons  using example.tex, booktabs scalars("N_g Groups" "ll Log likelihood" "lrtest_chi2 LR test Chi$^2$") label se replace  ///
>                  alignment(D{c -(}.{c )-}{c -(}.{c )-}{c -(}-1{c )-}) page(dcolumn) nonumber mtitle nodepvars ///
>                  title(Mixed Effects Logistic Regression (Conservative Claims)  \label{c -(}tabcons2{c )-}) ///
>                  mtitles("Model I" "Model II" "Model III" "Model IV") ///
>                  indicate("Congress Year Dummies = *cnrptyy" ) ///
>                  addnotes("Coefficients are Log Odds. All independent variables are coded 0 to 1.") ///
>                  order(protest 1.poorhc 2.poorhc 1.nonwhitec 2.nonwhitec 1.noorgigc 2.noorgigc educ dem lengthserve votemargin blkrep hisprep womanrep) ///
>                  star(* 0.10 ** 0.05 *** 0.01) nobaselevels nodepvars
{res}{txt}(output written to {browse  `"example.tex"'})

{com}. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/LaGina/Desktop/bjps_costlyprotest_replicationfile.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}27 Jun 2020, 19:27:20
{txt}{.-}
{smcl}
{txt}{sf}{ul off}